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Larger right inferior frontal gyrus volume and surface area in participants at genetic risk for bipolar disorders

Published online by Cambridge University Press:  30 July 2018

V. Drobinin ,
C. Slaney ,
J. Garnham ,
L. Propper ,
R. Uher Open the ORCID record for R. Uher [Opens in a new window] ,
M. Alda Open the ORCID record for M. Alda [Opens in a new window]  and
T. Hajek
V. Drobinin
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada Department of Medical Neuroscience, Dalhousie University, Halifax, Canada
C. Slaney
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada
J. Garnham
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada
L. Propper
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada
R. Uher
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada Department of Medical Neuroscience, Dalhousie University, Halifax, Canada
M. Alda
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada
T. Hajek*
Affiliation:
Department of Psychiatry, Dalhousie University, Halifax, Canada
*
Author for correspondence: T. Hajek, E-mail: tomas.hajek@dal.ca
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Abstract

Background

Larger grey matter volume of the inferior frontal gyrus (IFG) is among the most replicated biomarkers of genetic risk for bipolar disorders (BD). However, the IFG is a heterogeneous prefrontal region, and volumetric findings can be attributable to changes in cortical thickness (CT), surface area (SA) or gyrification. Here, we investigated the morphometry of IFG in participants at genetic risk for BD.

Methods

We quantified the IFG cortical grey matter volume in 29 affected, 32 unaffected relatives of BD probands, and 42 controls. We then examined SA, CT, and cortical folding in subregions of the IFG.

Results

We found volumetric group differences in the right IFG, with the largest volumes in unaffected high-risk and smallest in control participants (F2,192 = 3.07, p = 0.01). The volume alterations were localized to the pars triangularis of the IFG (F2,97 = 4.05, p = 0.02), with no differences in pars opercularis or pars orbitalis. Pars triangularis volume was highly correlated with its SA [Pearson r(101) = 0.88, p < 0.001], which significantly differed between the groups (F2,97 = 4.45, p = 0.01). As with volume, the mean SA of the pars triangularis was greater in unaffected (corrected p = 0.02) and affected relatives (corrected p = 0.05) compared with controls. We did not find group differences in pars triangularis CT or gyrification.

Conclusions

These findings strengthen prior knowledge about the volumetric findings in this region and provide a new insight into the localization and topology of IFG alterations. The unique nature of rIFG morphology in BD, with larger volume and SA early in the course of illness, could have practical implications for detection of participants at risk for BD.

Keywords

Bipolar disorderscortical surface areacortical thicknessfamilial riskgyrificationinferior frontal gyruspars triangularis
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 8 , June 2019 , pp. 1308 - 1315
Copyright
Copyright © Cambridge University Press 2018 

Introduction

Bipolar disorders (BD) typically develop in late teens or early 20s and follow a recurrent course (Judd et al., Reference Judd, Akiskal, Schettler, Endicott, Maser, Solomon, Leon, Rice and Keller2002). The combination of the early age of onset and life-long course make BD one of the top causes of morbidity and disability worldwide (Judd et al., Reference Judd, Schettler, Solomon, Maser, Coryell, Endicott and Akiskal2008). While heritability estimates for BD are as high as 89% (McGuffin et al., Reference McGuffin, Rijsdijk, Andrew, Sham, Katz and Cardno2003; Song et al., Reference Song, Bergen, Kuja-Halkola, Larsson, Landén and Lichtenstein2015) there are no widely accepted biological markers of the disorder and diagnosis is made based on behavioural symptoms. This in part contributes to the fact that correct diagnosis often lags behind symptom onset by up to a decade (Ghaemi et al., Reference Ghaemi, Sachs, Chiou, Pandurangi and Goodwin1999; Bschor et al., Reference Bschor, Angst, Azorin, Bowden, Perugi, Vieta, Young and Krüger2012). Therefore, it is imperative to identify biological markers of the disorder to assist with early diagnosis and to inform treatment (Conus et al., Reference Conus, Macneil and McGorry2014).

Neuroimaging holds substantial promise in investigating the neurobiology of psychiatric disorders. The largest psychiatric neuroimaging study to date has found structural brain differences in frontal, temporal and parietal regions in BD patients compared with healthy controls (Hibar et al., Reference Hibar, Westlye, Doan, Jahanshad, Cheung, Ching, Versace, Bilderbeck, Uhlmann, Mwangi, Krämer, Overs, Hartberg, Abé, Dima, Grotegerd, Sprooten, Bøen, Jimenez, Howells, Delvecchio, Temmingh, Starke, Almeida, Goikolea, Houenou, Beard, Rauer, Abramovic, Bonnin, Ponteduro, Keil, Rive, Yao, Yalin, Najt, Rosa, Redlich, Trost, Hagenaars, Fears, Alonso-Lana, van Erp, Nickson, Chaim-Avancini, Meier, Elvsåshagen, Haukvik, Lee, Schene, Lloyd, Young, Nugent, Dale, Pfennig, McIntosh, Lafer, Baune, Ekman, Zarate, Bearden, Henry, Simhandl, McDonald, Bourne, Stein, Wolf, Cannon, Glahn, Veltman, Pomarol-Clotet, Vieta, Canales-Rodriguez, Nery, Duran, Busatto, Roberts, Pearlson, Goodwin, Kugel, Whalley, Ruhe, Soares, Fullerton, Rybakowski, Savitz, Chaim, Fatjó-Vilas, Soeiro-de-Souza, Boks, Zanetti, Otaduy, Schaufelberger, Alda, Ingvar, Phillips, Kempton, Bauer, Landén, Lawrence, van Haren, Horn, Freimer, Gruber, Schofield, Mitchell, Kahn, Lenroot, Machado-Vieira, Ophoff, Sarró, Frangou, Satterthwaite, Hajek, Dannlowski, Malt, Arolt, Gattaz, Drevets, Caseras, Agartz, Thompson and Andreassen2017). At the same time, meta-analyses of structural cortical alterations in BD have emphasized the heterogeneity of findings across the literature (Selvaraj et al., Reference Selvaraj, Arnone, Job, Stanfield, Farrow, Nugent, Scherk, Gruber, Chen, Sachdev, Dickstein, Malhi, Ha, Ha, Phillips and McIntosh2012; Wise et al., Reference Wise, Radua, Via, Cardoner, Abe, Adams, Amico, Cheng, Cole, de Azevedo Marques Périco, Dickstein, Farrow, Frodl, Wagner, Gotlib, Gruber, Ham, Job, Kempton, Kim, Koolschijn, Malhi, Mataix-Cols, McIntosh, Nugent, O'Brien, Pezzoli, Phillips, Sachdev, Salvadore, Selvaraj, Stanfield, Thomas, van Tol, van der Wee, Veltman, Young, Fu, Cleare and Arnone2017). The statistical heterogeneity likely reflects clinical heterogeneity, whereas brain changes in BD might not only represent the biological markers of the disorder, but also progressive effects of illness (Hajek et al., Reference Hajek, Cullis, Novak, Kopecek, Höschl, Blagdon, O'Donovan, Bauer, Young, Macqueen and Alda2012; Moylan et al., Reference Moylan, Maes, Wray and Berk2013), common comorbidities (Hajek et al., Reference Hajek, Calkin, Blagdon, Slaney and Alda2015a; Pavlova et al., Reference Pavlova, Perlis, Alda and Uher2015), and exposure to medications (Hajek et al., Reference Hajek, Bauer, Simhandl, Rybakowski, O'Donovan, Pfennig, König, Suwalska, Yucel, Uher, Young, MacQueen and Alda2014; Hajek and Weiner, Reference Hajek and Weiner2016). One strategy that lends itself well to isolating the biological risk factors inherent to BD is to use genetic high-risk (HR) design. This requires the study of healthy unaffected offspring of parents with BD, who are at high genetic risk but have not yet expressed any symptoms of the illness. Therefore, neurostructural alterations in HR individuals cannot be a consequence of the illness burden, comorbidities, or treatments.

In our previous replication design HR study, we found larger right inferior frontal gyrus (IFG) volumes among both unaffected HR subjects and affected familial participants across two centres (Hajek et al., Reference Hajek, Cullis, Novak, Kopecek, Blagdon, Propper, Stopkova, Duffy, Hoschl, Uher, Paus, Young and Alda2013b). Other studies have reported similar findings (Adler et al., Reference Adler, Levine, DelBello and Strakowski2005; Matsuo et al., Reference Matsuo, Kopecek, Nicoletti, Hatch, Watanabe, Nery, Zunta-Soares and Soares2012; Sarıçiçek et al., Reference Sarıçiçek, Yalın, Hıdıroğlu, Çavuşoğlu, Taş, Ceylan, Zorlu, Ada, Tunca and Özerdem2015; Roberts et al., Reference Roberts, Lenroot, Frankland, Yeung, Gale, Wright, Lau, Levy, Wen and Mitchell2016), making IFG volume change the most replicated neurostructural finding in participants at genetic risk for BD. Aside from the fact that IFG structural alterations are found in unaffected relatives, they are also associated with the illness. In addition, IFG volumes are heritable (Winkler et al., Reference Winkler, Kochunov, Blangero, Almasy, Zilles, Fox, Duggirala and Glahn2010). Thus, volumetric changes of IFG meet criteria for a psychiatric endophenotype (Gottesman and Gould, Reference Gottesman and Gould2003). The localization to the rIFG also has good face validity vis-à-vis its function. The right IFG is an area of the prefrontal cortex centrally involved in response inhibition (Aron et al., Reference Aron, Robbins and Poldrack2014). Impaired response inhibition underlies certain symptoms of mood disorders and is a candidate neurocognitive endophenotype for BD (Bora et al., Reference Bora, Yucel and Pantelis2009). Indeed, hypoactivations of rIFG are frequently found in BD during inhibitory control tasks, and are also present during euthymia (see Selvaraj et al., Reference Selvaraj, Arnone, Job, Stanfield, Farrow, Nugent, Scherk, Gruber, Chen, Sachdev, Dickstein, Malhi, Ha, Ha, Phillips and McIntosh2012; Hajek et al., Reference Hajek, Alda, Hajek and Ivanoff2013a for review).

Considering that IFG alterations are among the strongest candidates for a neuroanatomical signature of BD, it is important to better understand the underpinnings of these changes. Cortical grey matter volume is a product of cortical surface area (SA) and cortical thickness (CT). SA and CT have been shown to be genetically distinct (Panizzon et al., Reference Panizzon, Fennema-Notestine, Eyler, Jernigan, Prom-Wormley, Neale, Jacobson, Lyons, Grant, Franz, Xian, Tsuang, Fischl, Seidman, Dale and Kremen2009), influenced by different neurobiological mechanisms (Winkler et al., Reference Winkler, Kochunov, Blangero, Almasy, Zilles, Fox, Duggirala and Glahn2010) and sometimes affected in opposite directions (Lin et al., Reference Lin, Ching, Vajdi, Sun, Jonas, Jalbrzikowski, Kushan-Wells, Pacheco Hansen, Krikorian, Gutman, Dokoru, Helleman, Thompson and Bearden2017). Thus, examining SA and CT differences separately might lead to the identification of phylogenetically suitable markers that offer greater insight into the neurobiology of the disorder. Evolutionarily, gyrification (cortical folding) made possible the expansion of the cortex within the constraints of the skull and can now be accurately studied in-vivo (Schaer et al., Reference Schaer, Cuadra, Tamarit, Lazeyras, Eliez and Thiran2008). This morphological measure captures a developmental window distinct from CT (Schaer and Eliez, Reference Schaer and Eliez2009) and SA (Hogstrom et al., Reference Hogstrom, Westlye, Walhovd and Fjell2013). Studies of gyrification have yielded mixed results in BD (McIntosh et al., Reference McIntosh, Moorhead, McKirdy, Hall, Sussmann, Stanfield, Harris, Johnstone and Lawrie2009; Nenadic et al., Reference Nenadic, Maitra, Dietzek, Langbein, Smesny, Sauer and Gaser2015). Gyrification in individuals at familial risk for BD remains to be examined.

In the current HR design study, we attempted to replicate the finding of enlarged IFG in unaffected and affected offspring of BD parents relative to controls. In addition, we investigated the localization of volumetric changes within IFG and their topology, with regard to CT, SA, and gyrification.

Methods and materials

Participants

Participants were recruited from an ongoing Offspring Risk for BD Imaging Study–ORBIS. We recruited offspring from families of well-characterized adult BD probands who had participated in previous genetic and HR studies (Duffy et al., Reference Duffy, Alda, Kutcher, Cavazzoni, Robertson, Grof and Grof2002; Lopez de Lara et al., Reference Lopez de Lara, Jaitovich-Groisman, Cruceanu, Mamdani, Lebel, Yerko, Beck, Young, Rouleau, Grof, Alda and Turecki2010; Hajek et al., Reference Hajek, Cooke, Kopecek, Novak, Hoschl and Alda2015b) in Halifax, Nova Scotia. The inclusion criterion was 15–30 years of age, which represents the typical range of age at onset of BD according to cross-sectional and prospective studies (Duffy et al., Reference Duffy, Alda, Hajek and Grof2009; Ortiz et al., Reference Ortiz, Bradler, Slaney, Garnham, Ruzickova, O'Donovan, Hajek and Alda2011). Consequently, these individuals remain at a substantial risk of future onset of BD. Only the offspring, not the probands, participated in the MRI study. As this is a part of an ongoing study, the sample included here was larger and partially overlapping with the sample described in our previous publication – 103 v. 82 participants (Hajek et al., Reference Hajek, Cullis, Novak, Kopecek, Blagdon, Propper, Stopkova, Duffy, Hoschl, Uher, Paus, Young and Alda2013b).

In keeping with previous studies (Todd et al., Reference Todd, Reich, Petti, Joshi, DePAULO, Nurnberger and Reich1996; Duffy et al., Reference Duffy, Alda, Kutcher, Cavazzoni, Robertson, Grof and Grof2002), we included participants with BD type I or type II, but not with BD NOS as probands for this study. Similar to participants with BDI, the BDII individuals had a low prevalence of comorbid conditions and an episodic course of illness. Family studies focusing on similarly narrow diagnoses have generally found them to be part of the same genetic spectrum (Gershon et al., Reference Gershon, Hamovit, Guroff, Dibble, Leckman, Sceery, Targum, Nurnberger, Goldin and Bunney1982). Furthermore, neuroimaging studies show similar findings for patients with BD I and BD II (Hamakawa et al., Reference Hamakawa, Kato, Murashita and Kato1998; Winsberg et al., Reference Winsberg, Sachs, Tate, Adalsteinsson, Spielman and Ketter2000; Dager et al., Reference Dager, Friedman, Parow, Demopulos, Stoll, Lyoo, Dunner and Renshaw2004; McGrath et al., Reference McGrath, Wessels, Bell, Ulrich and Silverstone2004; Silverstone et al., Reference Silverstone, Asghar, O'Donnell, Ulrich and Hanstock2004; Wu et al., Reference Wu, O'Donnell, Ulrich, Asghar, Hanstock and Silverstone2004).

The offspring from BD probands were divided into two subgroups. (1) The unaffected HR group, which included 32 offspring without a personal history of Axis I psychiatric disorders. These individuals were considered HR because they came from multiplex families (more than one member affected with BD) and had one parent affected with a primary mood disorder. (2) The affected familial group, which included 29 offspring meeting criteria for a lifetime Axis I diagnosis of mood disorders (i.e. a personal history of at least one episode of depression, hypomania, or mania meeting full DSM-IV criteria) and had one parent affected with a primary mood disorder. Depressive episodes were included because unipolar depression is characteristically the first manifestation of illness in patients who later develop BD (Duffy et al., Reference Duffy, Alda, Kutcher, Cavazzoni, Robertson, Grof and Grof2002; Hillegers et al., Reference Hillegers, Reichart, Wals, Verhulst, Ormel and Nolen2005). Additionally, around 70% of depressed first-degree relatives of BD probands are estimated to suffer from BD (Blacker and Tsuang, Reference Blacker and Tsuang1993). Lastly, we recruited 42 control participants free of personal or family history of DSM-IV Axis I psychiatric disorders.

Common exclusion criteria for all groups were a personal history of (1) any serious medical or neurologic disorders, (2) substance abuse/dependence during the previous 6 months, or (3) magnetic resonance imaging (MRI) exclusion criteria.

Materials

Each participant received a complete description of the study and provided written informed consent. The study was approved by the Research Ethics Boards of the Izaak Walton Killam Health Center and the Nova Scotia Health Authority, Halifax, Nova Scotia.

Probands, offspring and control subjects were interviewed by pairs of clinicians (psychiatrists and/or nurses) using Schedule for Affective Disorders and Schizophrenia—Lifetime version (SADS-L) (Endicott and Spitzer, Reference Endicott and Spitzer1978) or Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetime version (KSADS-PL) (Kaufman et al., Reference Kaufman, Birmaher, Brent, Rao, Flynn, Moreci, Williamson and Ryan1997) for participants under 18 years of age. Diagnoses were made based on DSM-IV in a blind consensus review, by an independent panel of senior clinical researchers using all available clinical materials.

MRI acquisition

MRI acquisitions were performed with a 1.5-Tesla General Electric Signa scanner equipped with a single-channel head coil, located at the IWK Health Centre, Halifax, Nova Scotia. After a localizer scan, a T 1-weighted spoiled gradient recalled (SPGR) scan was acquired with the following parameters: flip angle 40°, echo time 5 ms, repetition time 25 ms, field of view 24 cm × 18 cm, matrix 256 × 160 pixels, number of excitations = 1, no interslice gap, 124 coronal 1.5 mm thick slices.

MRI analysis

The structural T 1-weighted scans were analyzed using FreeSurfer v5.3 (Fischl et al., Reference Fischl, van der Kouwe, Destrieux, Halgren, Ségonne, Salat, Busa, Seidman, Goldstein, Kennedy, Caviness, Makris, Rosen and Dale2004). Briefly, FreeSurfer processing included automated skull stripping, bias field correction, non-linear registration with a stereotaxic atlas and grey-white matter segmentation and generation of cortical surface models (for more detail see: https://surfer.nmr.mgh.harvard.edu). Utilizing these models, an automated labelling system subdivided the cerebral cortex into gyral-based regions of interest (ROIs) corresponding to the Desikan–Killiany Freesurfer atlas (Desikan et al., Reference Desikan, Ségonne, Fischl, Quinn, Dickerson, Blacker, Buckner, Dale, Maguire, Hyman, Albert and Killiany2006). For each participant, we extracted cortical grey matter volume, thickness, and SA for three subdivisions of the bilateral IFG: pars orbitalis, pars triangularis, pars opercularis.

Furthermore, the pial surface reconstruction allowed us to measure the 3D local gyrification index (lGI). The lGI measure provides a ratio of the convex hull (outer) cortex SA to buried cortex SA. Thus, a greater lGI represents greater gyrification, or more folding in each ROI and vice versa. Details of the automated lGI computation can be found in the validation paper (Schaer et al., Reference Schaer, Cuadra, Tamarit, Lazeyras, Eliez and Thiran2008) and on the FreeSurfer website https://surfer.nmr.mgh.harvard.edu/fswiki/LGI.

Raw images were inspected for motion, low contrast, and other artifacts. FreeSurfer output was manually examined for skull stripping and segmentation errors. If found, errors were manually corrected and the affected scans re-processed.

Statistical analyses

Statistical analyses were performed in R Studio (R version 3.3.3). Demographic and clinical variables were compared with one-way Analysis of Variance (ANOVA) or a χ2 test for categorical variables. For the primary analysis, we investigated group differences in bilateral IFG volume. For this, we used Multivariate Analysis of Variance (MANOVA), with the IFG volume in individual IFG subdivisions (pars orbitalis, pars triangularis, pars opercularis) as the dependent variable, group status as an independent variable, age, sex, and estimated total intracranial volume (TIV) (Buckner et al., Reference Buckner, Head, Parker, Fotenos, Marcus, Morris and Snyder2004) as covariates. In follow-up one-way ANOVAs we explored the localization of volumetric changes within the IFG. Volumes of IFG subdivisions served as dependent variables with group status as the independent variable and age, sex, and TIV as covariates. We controlled for multiple comparisons with the Benjamini-Hochberg procedure (Benjamini and Hochberg, Reference Benjamini and Hochberg1995) and report corrected p values. In subregions with significant between-group volumetric differences, we further explored which topographical features most contributed to these alternations (CS, SA, gyrification). We used identical model specifications as above for these exploratory ANOVAs followed by Bonferroni corrected pair-wise comparisons using Tukey's Honest Significant Difference (HSD) test to identify which groups were driving the significant omnibus findings. We also used Pearson's correlation coefficient to quantify the strength of the relationship between regional volumetric differences and corresponding measures of SA, thickness, and gyrification.

As several participants in the study were biologically related, we did sensitivity testing of significant results using mixed effect generalized linear models structured identically to the ANOVA but with the additional inclusion of family membership as a random effect. Effect sizes are summarized with partial eta squared (η p2) and its bootstrapped 95% confidence interval (CI) over 1000 simulations.

Results

We recruited 32 unaffected HR, 29 affected familial, and 42 control participants. The groups did not differ in the proportion of females or intracranial volumes, but control participants were older than the unaffected HR participants (see Table 1).

Table 1. Description of the participants

AAP, atypical antipsychotics; AC, anticonvulsant; AD, antidepressants; ADD, adjustment disorder; ADHD, attention-deficit/hyperactivity disorder; ADO, anxiety disorder; BD, bipolar disorder; EO, eating disorder; HR, high risk; Li, lithium; MD, major depression; NA, not applicable; NOS, not otherwise specified; ns, nonsignificant, PS, psychostimulant; SUD substance use disorder.

a BD NOS participants met criteria for major depressive episodes and had subsyndromal hypomanic symptoms.

b Data missing in two participants.

When controlling for age and sex, MANOVA revealed that the unaffected, affected, and control participants differed in the right (F 2,192 = 3.07, p = 0.01), but not the left (F 2,192 = 1.61, p = 0.29), IFG volumes. The overall rIFG volume was largest in unaffected and smallest in control participants.

Within the right IFG, we found the largest volume differences in the pars triangularis (F 2,97 = 4.05, p = 0.02, η p2 = 0.08, 95% CI 0.001–0.18), see Fig. 1. Post hoc comparisons using the Tukey HSD test showed the volume of the right pars triangularis was greater in unaffected HR (M = 5110.88 mm3, s.d. = 1118.43 mm3, p = 0.02) and affected familial groups (M = 4915.17 mm3, s.d. = 798.88 mm3, p = 0.05) compared with control participants (M = 4400.67 mm3, s.d. = 771.70 mm3). The group differences in pars triangularis remained significant when we controlled for family membership (F 2,97 = 3.12, p = 0.05). There were no significant volumetric differences in the pars opercularis (F 2,97 = 2.85, p = 0.06, η p2 = 0.006, 95% CI 0–0.15) or the pars orbitalis (F 2, 97 = 1.71, p = 0.2, η p2 = 0.03, 95% CI 0–0.12).

Fig. 1. Larger pars triangularis volume and surface area in affected familial and unaffected High-Risk groups relative to control participants. Means ± s.e.m. (a) Anatomical subdivisions of the inferior frontal gyrus (IFG). Significant group differences were localized to pars triangularis. (b) Volume of pars triangularis. (c) Surface area of pars triangularis.

The volume of the right pars triangularis was highly correlated with its SA [Pearson r(101) = 0.88, p < 0.001] and ANOVA revealed significant group differences in pars triangularis SA (F 2, 97 = 4.45, p = 0.01, η p2 = 0.08, 95% CI 0.003–0.19) Post hoc comparisons using the Tukey HSD test showed that as with volume, the mean SA of the right pars triangularis was greater in unaffected HR (M = 1588.31 mm2, s.d. = 313.29 mm2, p = 0.02) and affected familial groups (M = 1565.38 mm2, s.d. = 253.33 mm2, p = 0.03) compared with control participants (M = 1413.38 mm2, s.d. = 221.22 mm2).

The correlation between the right pars triangularis grey matter volume and CT was much lower than for SA, but still significant [Pearson r(101) = 0.28, p = 0.005]. However, we did not find differences between the groups in CT (F 2, 97 = 0.05, p = 0.95, η p2 = 0.001, 95% CI 0–0.01) of the pars triangularis. As expected SA and CT of this region were not associated [Pearson r(101) = −0.16, p = 0.11].

Pars triangularis volume and gyrification were not significantly correlated [Pearson r(101) = –0.13, p = 0.19]. Likewise the groups did not differ in gyrification (F 2,97 = 0.06, p = 0.94, η p2 = 0.001, 95% CI 0–0.018) of the pars triangularis.

Discussion

In this study, we replicated and extended the finding of increased right IFG volume as a neuroanatomical marker of genetic susceptibility for BD. Using 3D representations of the cortical sheet we found that the volumetric enlargement was linked to the increased cortical SA, and not CT or cortical folding. Moreover, we localized the largest volume and SA differences to the pars triangularis of the rIFG, providing a more sensitive marker of genetic risk for BD.

Our finding of larger rIFG follows a well-established pattern of evidence implicating this region of the prefrontal cortex in BD. The direction of the finding may seem surprising, but regional cortical volume increases have been reported in other HR and early-course BD imaging studies (Kempton et al., Reference Kempton, Haldane, Jogia, Grasby, Collier and Frangou2009; Frangou, Reference Frangou2011; Adleman et al., Reference Adleman, Fromm, Razdan, Kayser, Dickstein, Brotman, Pine and Leibenluft2012; Bauer et al., Reference Bauer, Sanches, Suchting, Green, El Fangary, Zunta–Soares and Soares2014; Lin et al., Reference Lin, Xu, Wong, Wu, Li, Lu, Chen, Chen, Lai, Zhong, So and Lee2015). Increased IFG grey matter volume has previously been found in the early course of BD, in patients with the first episode of mania (Adler et al., Reference Adler, Levine, DelBello and Strakowski2005). Our previous exploratory, replication design, multi-centre study has shown larger rIFG volumes in unaffected HR, affected familial as well as in a third group comprising a clinical sample of young BD participants (Hajek et al., Reference Hajek, Cullis, Novak, Kopecek, Blagdon, Propper, Stopkova, Duffy, Hoschl, Uher, Paus, Young and Alda2013b). Similar findings were replicated in more recent studies, showing increased IFG volumes in both euthymic BD patients and healthy first-degree relatives when compared with healthy controls (Sarıçiçek et al., Reference Sarıçiçek, Yalın, Hıdıroğlu, Çavuşoğlu, Taş, Ceylan, Zorlu, Ada, Tunca and Özerdem2015; Roberts et al., Reference Roberts, Lenroot, Frankland, Yeung, Gale, Wright, Lau, Levy, Wen and Mitchell2016). In addition, the IFG has been shown to be sensitive to illness chronicity, with studies finding a significant negative correlation between the duration of illness and grey matter volume in this region (Matsuo et al., Reference Matsuo, Kopecek, Nicoletti, Hatch, Watanabe, Nery, Zunta-Soares and Soares2012; Hajek et al., Reference Hajek, Cullis, Novak, Kopecek, Blagdon, Propper, Stopkova, Duffy, Hoschl, Uher, Paus, Young and Alda2013b).

In this study, we found that the IFG neurostructural alterations were most pronounced in the pars triangularis, a finding in concordance with the area's functional profile. Studies in healthy individuals and lesion studies have shown that the pars triangularis of the IFG is not only associated with but also necessary for successful response inhibition (Collette et al., Reference Collette, Van der Linden, Delfiore, Degueldre, Luxen and Salmon2001; Menon et al., Reference Menon, Adleman, White, Glover and Reiss2001; Aron et al., Reference Aron, Robbins and Poldrack2014). In fact, age-related improvements in activation of the pars triangularis within the frontostriatal network underlie improvements in the normative development of self-regulatory control (Marsh et al., Reference Marsh, Zhu, Schultz, Quackenbush, Royal, Skudlarski and Peterson2006). Impaired response inhibition is central to many symptoms of mania, such as increased risk-taking, impulsive behaviour, talkativeness, and excessive spending. Moreover, a meta-analysis of neurocognitive studies in at-risk participants revealed response inhibition as the most prominent endophenotype of BD (Bora et al., Reference Bora, Yucel and Pantelis2009). As would be expected, BD is marked by diminished activity in the IFG, particularly during response inhibition to emotional stimuli (Hajek et al., Reference Hajek, Alda, Hajek and Ivanoff2013a) and during reward processing (Singh et al., Reference Singh, Kelley, Howe, Reiss, Gotlib and Chang2014). Hypoactivity in the IFG has been reported in both BD patients and youth at HR for the disorder as well as during manic and euthymic states (Foland-Ross et al., Reference Foland-Ross, Bookheimer, Lieberman, Sugar, Townsend, Fischer, Torrisi, Penfold, Madsen, Thompson and Altshuler2012; Townsend et al., Reference Townsend, Bookheimer, Foland-Ross, Moody, Eisenberger, Fischer, Cohen, Sugar and Altshuler2012; Roberts et al., Reference Roberts, Green, Breakspear, McCormack, Frankland, Wright, Levy, Lenroot, Chan and Mitchell2013), further corroborating the endophenotypic nature of the rIFG alterations.

Cortical grey matter volume is a product of CT and SA. Evidence suggests that CT is determined by asymmetric division of radial glia progenitors and reflects the number of cells within cortical columns, while SA is linked to symmetric division of progenitor cells in the ventricular and sub ventricular layers and relates to the quantity of cortical columns (Rakic et al., Reference Rakic, Ayoub, Breunig and Dominguez2009; Florio and Huttner, Reference Florio and Huttner2014; Wierenga et al., Reference Wierenga, Langen, Oranje and Durston2014). Thus, the two measures develop with distinct cellular mechanisms and show almost no genetic correlation (Panizzon et al., Reference Panizzon, Fennema-Notestine, Eyler, Jernigan, Prom-Wormley, Neale, Jacobson, Lyons, Grant, Franz, Xian, Tsuang, Fischl, Seidman, Dale and Kremen2009). The finding that increased pars triangularis volume was driven by SA and not CT is in line with the general finding that individual variation in human cortical volume is more attributable to variation in SA than CT (Im et al., Reference Im, Lee, Lyttelton, Kim, Evans and Kim2008). Furthermore, the SA of the right pars triangularis is significantly more heritable than its CT (Winkler et al., Reference Winkler, Kochunov, Blangero, Almasy, Zilles, Fox, Duggirala and Glahn2010). Our finding is further supported by a recent BD twin-study showing that genes influencing BD are associated with regional increases in SA (Bootsman et al., Reference Bootsman, Brouwer, Schnack, van Baal, van der Schot, Vonk, Hulshoff Pol, Nolen, Kahn and van Haren2015). Accordingly, a recent pilot study utilized regional increases in SA to distinguish BD from major depressive disorders (Fung et al., Reference Fung, Deng, Zhao, Li, Qu, Li, Zeng, Jin, Ma, Yu, Wang, Shum and Chan2015).

Regional increases in brain volume or SA have been attributed to resilience (Ladouceur et al., Reference Ladouceur, Almeida, Birmaher, Axelson, Nau, Kalas, Monk, Kupfer and Phillips2008), neuropathology (Adler et al., Reference Adler, Levine, DelBello and Strakowski2005), effects of medication (Yucel et al., Reference Yucel, McKinnon, Taylor, Macdonald, Alda, Young and MacQueen2007; Hajek et al., Reference Hajek, Bauer, Simhandl, Rybakowski, O'Donovan, Pfennig, König, Suwalska, Yucel, Uher, Young, MacQueen and Alda2014), or disrupted maturation (Konarski et al., Reference Konarski, McIntyre, Kennedy, Rafi-Tari, Soczynska and Ketter2008). The larger pars triangularis volume and SA is unlikely to be a marker of resilience as it was also larger in already affected participants. Furthermore, resilience would be difficult to infer from the current status of unaffected HR participants as they are passing through the HR age range and can develop BD in the future. Neuropathology, such as hypertrophy due to preapoptotic edema is doubtful since our previous spectroscopy work with a partially overlapping sample showed similar metabolite concentrations surrounding the IFG between unaffected HR and control participants (Hajek et al., Reference Hajek, Bernier, Slaney, Propper, Schmidt, Carrey, MacQueen, Duffy and Alda2008). Even more importantly, edema would likely lead to changes in both CT and SA. Neurotrophic effects of medication, such as lithium, need to be considered. However, in our sample, the largest structural differences were found in the unaffected HR participants who were medication naive. Normative brain maturation from childhood to young adulthood involves grey matter reductions across the cortex. It has been suggested that increased grey matter volumes in BD offspring might be indicative of disruptions in normative brain growth, for example, through an aberrant synaptic remodeling (Sowell et al., Reference Sowell, Peterson, Thompson, Welcome, Henkenius and Toga2003; Herting et al., Reference Herting, Gautam, Spielberg, Dahl and Sowell2015). However, while disrupted maturation is plausible, we controlled for age and prospective studies would be more suitable in inferring developmental trajectories.

The strengths of this study are its hypothesis-driven approach, young sample, and genetic HR design. We have conducted a targeted investigation of the IFG because of its strong previously established involvement in BD. Furthermore, we investigated this ROI in a relatively large dataset of 103 brain scans. The participants were mostly in their 20s, recruited from the age range during which transition to BD approaches peak incidence. We benefitted from also scanning unaffected offspring of BD probands because neuroanatomical changes have previously been linked to confounds such as illness burden, common comorbidities, and effects of treatment, none of which apply to the unaffected group.

There are several limitations of this study. While we characterized the structural phenotype and topology of IFG alterations in BD, we did not include measures of cognitive functioning or genetic markers. Future work should investigate the relationship between pars triangularis structural change with neurocognitive test performance. In addition, genotyping data would allow future studies to test whether the association between IFG structural change is moderated by a polygenic risk score for BD. Furthermore, our study was not designed to capture the effects of sub-syndromal features. There was no association between cortical measures and clinical scales (online Supplementary Figure).

Our hypothesis-driven analysis was focused on the IFG, however, this is not the only region involved in the disorder (Hibar et al., Reference Hibar, Westlye, Doan, Jahanshad, Cheung, Ching, Versace, Bilderbeck, Uhlmann, Mwangi, Krämer, Overs, Hartberg, Abé, Dima, Grotegerd, Sprooten, Bøen, Jimenez, Howells, Delvecchio, Temmingh, Starke, Almeida, Goikolea, Houenou, Beard, Rauer, Abramovic, Bonnin, Ponteduro, Keil, Rive, Yao, Yalin, Najt, Rosa, Redlich, Trost, Hagenaars, Fears, Alonso-Lana, van Erp, Nickson, Chaim-Avancini, Meier, Elvsåshagen, Haukvik, Lee, Schene, Lloyd, Young, Nugent, Dale, Pfennig, McIntosh, Lafer, Baune, Ekman, Zarate, Bearden, Henry, Simhandl, McDonald, Bourne, Stein, Wolf, Cannon, Glahn, Veltman, Pomarol-Clotet, Vieta, Canales-Rodriguez, Nery, Duran, Busatto, Roberts, Pearlson, Goodwin, Kugel, Whalley, Ruhe, Soares, Fullerton, Rybakowski, Savitz, Chaim, Fatjó-Vilas, Soeiro-de-Souza, Boks, Zanetti, Otaduy, Schaufelberger, Alda, Ingvar, Phillips, Kempton, Bauer, Landén, Lawrence, van Haren, Horn, Freimer, Gruber, Schofield, Mitchell, Kahn, Lenroot, Machado-Vieira, Ophoff, Sarró, Frangou, Satterthwaite, Hajek, Dannlowski, Malt, Arolt, Gattaz, Drevets, Caseras, Agartz, Thompson and Andreassen2017) and BD biomarkers will be further enhanced by incorporating information from additional brain regions and their interactions, in a multivariate statistical-learning framework. Interestingly, previous machine-learning work has also supported the importance of IFG structure in identifying participants at risk for BD (Hajek et al., Reference Hajek, Cooke, Kopecek, Novak, Hoschl and Alda2015b; Roberts et al., Reference Roberts, Lord, Frankland, Wright, Lau, Levy, Lenroot, Mitchell and Breakspear2017).

Currently, it is unknown if the pattern of SA increases without CT changes is dynamic through the development and course of illness. Future prospective longitudinal studies can provide additional context into the developmental window of BD. Finally, a proportion of our affected familial and unaffected HR participants were related thus introducing non-independence of cortical structure. Our main findings remained unchanged when we controlled for this by implementing mixed effects models with family membership as a random effect.

In summary, we expanded the finding of increased rIFG volume as a marker of genetic risk for BD. We localized the largest group differences to the enlargement of the right pars triangularis and found that volumetric change was driven mainly by SA rather than CT or folding. These findings strengthen prior knowledge about the volumetric alterations in this region and provide new insight into the localization and topology of IFG alterations, which aid in better understanding of brain risk factors associated with BD.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S0033291718001903

Acknowledgements

This study was supported by grants from the Canadian Institutes of Health Research (grants 103703 and 106469); Nova Scotia Health Research Foundation; and Dalhousie Clinical Research Scholarship to Dr Hajek.

Conflict of interest

None of the authors has any conflicts of interest to disclose.

Ethical standards

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

References

Adleman, NE, Fromm, SJ, Razdan, V, Kayser, R, Dickstein, DP, Brotman, MA, Pine, DS and Leibenluft, E (2012) Cross-sectional and longitudinal abnormalities in brain structure in children with severe mood dysregulation or bipolar disorder. Journal of Child Psychology and Psychiatry and Allied Disciplines 53, 1149–1156.Google Scholar
Adler, CM, Levine, AD, DelBello, MP and Strakowski, SM (2005) Changes in gray matter volume in patients with bipolar disorder. Biological Psychiatry 58, 151–157.Google Scholar
Aron, AR, Robbins, TW and Poldrack, RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends in Cognitive Sciences 18, 177–185.Google Scholar
Bauer, IE, Sanches, M, Suchting, R, Green, CE, El Fangary, NM, Zunta–Soares, GB and Soares, JC (2014) Amygdala enlargement in unaffected offspring of bipolar parents. Journal of Psychiatric Research 59, 200–205.Google Scholar
Benjamini, Y and Hochberg, Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300.Google Scholar
Blacker, D and Tsuang, MT (1993) Unipolar relatives in bipolar pedigrees: are they bipolar? Psychiatric Genetics 3, 5–16.Google Scholar
Bootsman, F, Brouwer, RM, Schnack, HG, van Baal, GCM, van der Schot, AC, Vonk, R, Hulshoff Pol, HE, Nolen, WA, Kahn, RS and van Haren, NEM (2015) Genetic and environmental influences on cortical surface area and cortical thickness in bipolar disorder. Psychological Medicine 45, 193–204.Google Scholar
Bora, E, Yucel, M and Pantelis, C (2009) Cognitive endophenotypes of bipolar disorder: a meta-analysis of neuropsychological deficits in euthymic patients and their first-degree relatives. Journal of Affective Disorders 113, 1–20.Google Scholar
Bschor, T, Angst, J, Azorin, JM, Bowden, CL, Perugi, G, Vieta, E, Young, AH and Krüger, S (2012) Are bipolar disorders underdiagnosed in patients with depressive episodes? Results of the multicenter BRIDGE screening study in Germany. Journal of Affective Disorders 142, 45–52.Google Scholar
Buckner, RL, Head, D, Parker, J, Fotenos, AF, Marcus, D, Morris, JC and Snyder, AZ (2004) A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23, 724–738.Google Scholar
Collette, F, Van der Linden, M, Delfiore, G, Degueldre, C, Luxen, A and Salmon, E (2001) The functional anatomy of inhibition processes investigated with the Hayling task. NeuroImage 14, 258–267.Google Scholar
Conus, P, Macneil, C and McGorry, PD (2014) Public health significance of bipolar disorder: implications for early intervention and prevention. Bipolar Disorders 16, 548–556.Google Scholar
Dager, SR, Friedman, SD, Parow, A, Demopulos, C, Stoll, AL, Lyoo, IK, Dunner, DL and Renshaw, PF (2004) Brain metabolic alterations in medication-free patients with bipolar disorder. Archives of General Psychiatry 61, 450–458.Google Scholar
Desikan, RS, Ségonne, F, Fischl, B, Quinn, BT, Dickerson, BC, Blacker, D, Buckner, RL, Dale, AM, Maguire, RP, Hyman, BT, Albert, MS and Killiany, RJ (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980.Google Scholar
Duffy, A, Alda, M, Hajek, T and Grof, P (2009) Early course of bipolar disorder in high-risk offspring: prospective study. The British Journal of Psychiatry: The Journal of Mental Science 195, 457–458.Google Scholar
Duffy, A, Alda, M, Kutcher, S, Cavazzoni, P, Robertson, C, Grof, E and Grof, P (2002) A prospective study of the offspring of bipolar parents responsive and nonresponsive to lithium treatment. The Journal of Clinical Psychiatry 63, 1171–1178.Google Scholar
Endicott, J and Spitzer, RL (1978) A diagnostic interview: the schedule for affective disorders and schizophrenia. Archives of General Psychiatry 35, 837–844.Google Scholar
Fischl, B, van der Kouwe, A, Destrieux, C, Halgren, E, Ségonne, F, Salat, DH, Busa, E, Seidman, LJ, Goldstein, J, Kennedy, D, Caviness, V, Makris, N, Rosen, B and Dale, AM (2004) Automatically parcellating the human cerebral cortex. Cerebral Cortex 14, 11–22.Google Scholar
Florio, M and Huttner, WB (2014) Neural progenitors, neurogenesis and the evolution of the neocortex. Development 141, 2182–2194.Google Scholar
Foland-Ross, LC, Bookheimer, SY, Lieberman, MD, Sugar, CA, Townsend, JD, Fischer, J, Torrisi, S, Penfold, C, Madsen, SK, Thompson, PM and Altshuler, LL (2012) Normal amygdala activation but deficient ventrolateral prefrontal activation in adults with bipolar disorder during euthymia. NeuroImage 59, 738–744.Google Scholar
Frangou, S (2011) Brain structural and functional correlates of resilience to bipolar disorder. Frontiers in Human Neuroscience 5, 184.Google Scholar
Fung, G, Deng, Y, Zhao, Q, Li, Z, Qu, M, Li, K, Zeng, Y, Jin, Z, Ma, Y, Yu, X, Wang, Z, Shum, DHK and Chan, RCK (2015) Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study. BMC Psychiatry 15, 298.Google Scholar
Gershon, ES, Hamovit, J, Guroff, JJ, Dibble, E, Leckman, JF, Sceery, W, Targum, SD, Nurnberger, JI, Goldin, LR and Bunney, WE (1982) A family study of schizoaffective, bipolar I, bipolar II, unipolar, and normal control probands. Archives of General Psychiatry 39, 1157–1167.Google Scholar
Ghaemi, SN, Sachs, GS, Chiou, AM, Pandurangi, AK and Goodwin, K (1999) Is bipolar disorder still underdiagnosed? Are antidepressants overutilized? Journal of Affective Disorders 52, 135–144.Google Scholar
Gottesman, II and Gould, TD (2003) The endophenotype concept in psychiatry: etymology and strategic intentions. American Journal of Psychiatry 160, 636–645.Google Scholar
Hajek, T, Alda, M, Hajek, E and Ivanoff, J (2013 a) Functional neuroanatomy of response inhibition in bipolar disorders--combined voxel based and cognitive performance meta-analysis. Journal of Psychiatric Research 47, 1955–1966.Google Scholar
Hajek, T, Bauer, M, Simhandl, C, Rybakowski, J, O'Donovan, C, Pfennig, A, König, B, Suwalska, A, Yucel, K, Uher, R, Young, LT, MacQueen, G and Alda, M (2014) Neuroprotective effect of lithium on hippocampal volumes in bipolar disorder independent of long-term treatment response. Psychological Medicine 44, 507–517.Google Scholar
Hajek, T, Bernier, D, Slaney, C, Propper, L, Schmidt, M, Carrey, N, MacQueen, G, Duffy, A and Alda, M (2008) A comparison of affected and unaffected relatives of patients with bipolar disorder using proton magnetic resonance spectroscopy. Journal of Psychiatry & Neuroscience 33, 531–540.Google Scholar
Hajek, T, Calkin, C, Blagdon, R, Slaney, C and Alda, M (2015 a) Type 2 diabetes mellitus: a potentially modifiable risk factor for neurochemical brain changes in bipolar disorders. Biological Psychiatry 77, 295–303.Google Scholar
Hajek, T, Cooke, C, Kopecek, M, Novak, T, Hoschl, C and Alda, M (2015 b) Using structural MRI to identify individuals at genetic risk for bipolar disorders: a 2-cohort, machine learning study. Journal of Psychiatry & Neuroscience: JPN 40, 316–324.Google Scholar
Hajek, T, Cullis, J, Novak, T, Kopecek, M, Blagdon, R, Propper, L, Stopkova, P, Duffy, A, Hoschl, C, Uher, R, Paus, T, Young, LT and Alda, M (2013 b) Brain structural signature of familial predisposition for bipolar disorder: replicable evidence for involvement of the right inferior frontal gyrus. Biological Psychiatry 73, 144–152.Google Scholar
Hajek, T, Cullis, J, Novak, T, Kopecek, M, Höschl, C, Blagdon, R, O'Donovan, C, Bauer, M, Young, LT, Macqueen, G and Alda, M (2012) Hippocampal volumes in bipolar disorders: opposing effects of illness burden and lithium treatment. Bipolar Disorders 14, 261–270.Google Scholar
Hajek, T and Weiner, MW (2016) Neuroprotective effects of lithium in human brain? Food for thought. Current Alzheimer Research 13, 862–872.Google Scholar
Hamakawa, H, Kato, T, Murashita, J and Kato, N (1998) Quantitative proton magnetic resonance spectroscopy of the basal ganglia in patients with affective disorders. European Archives of Psychiatry and Clinical Neuroscience 248, 53–58.Google Scholar
Herting, MM, Gautam, P, Spielberg, JM, Dahl, RE and Sowell, ER (2015) A longitudinal study: changes in cortical thickness and surface area during pubertal maturation. PLoS ONE 10, 1–14.Google Scholar
Hibar, DP, Westlye, LT, Doan, NT, Jahanshad, N, Cheung, JW, Ching, CRK, Versace, A, Bilderbeck, AC, Uhlmann, A, Mwangi, B, Krämer, B, Overs, B, Hartberg, CB, Abé, C, Dima, D, Grotegerd, D, Sprooten, E, Bøen, E, Jimenez, E, Howells, FM, Delvecchio, G, Temmingh, H, Starke, J, Almeida, JRC, Goikolea, JM, Houenou, J, Beard, LM, Rauer, L, Abramovic, L, Bonnin, M, Ponteduro, MF, Keil, M, Rive, MM, Yao, N, Yalin, N, Najt, P, Rosa, PG, Redlich, R, Trost, S, Hagenaars, S, Fears, SC, Alonso-Lana, S, van Erp, TGM, Nickson, T, Chaim-Avancini, TM, Meier, TB, Elvsåshagen, T, Haukvik, UK, Lee, WH, Schene, AH, Lloyd, AJ, Young, AH, Nugent, A, Dale, AM, Pfennig, A, McIntosh, AM, Lafer, B, Baune, BT, Ekman, CJ, Zarate, CA, Bearden, CE, Henry, C, Simhandl, C, McDonald, C, Bourne, C, Stein, DJ, Wolf, DH, Cannon, DM, Glahn, DC, Veltman, DJ, Pomarol-Clotet, E, Vieta, E, Canales-Rodriguez, EJ, Nery, FG, Duran, FLS, Busatto, GF, Roberts, G, Pearlson, GD, Goodwin, GM, Kugel, H, Whalley, HC, Ruhe, HG, Soares, JC, Fullerton, JM, Rybakowski, JK, Savitz, J, Chaim, KT, Fatjó-Vilas, M, Soeiro-de-Souza, MG, Boks, MP, Zanetti, MV, Otaduy, MCG, Schaufelberger, MS, Alda, M, Ingvar, M, Phillips, ML, Kempton, MJ, Bauer, M, Landén, M, Lawrence, NS, van Haren, NEM, Horn, NR, Freimer, NB, Gruber, O, Schofield, PR, Mitchell, PB, Kahn, RS, Lenroot, R, Machado-Vieira, R, Ophoff, RA, Sarró, S, Frangou, S, Satterthwaite, TD, Hajek, T, Dannlowski, U, Malt, UF, Arolt, V, Gattaz, WF, Drevets, WC, Caseras, X, Agartz, I, Thompson, PM and Andreassen, OA (2018) Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group. Molecular Psychiatry 23, 932–942.Google Scholar
Hillegers, MH, Reichart, CG, Wals, M, Verhulst, FC, Ormel, J and Nolen, WA (2005) Five-year prospective outcome of psychopathology in the adolescent offspring of bipolar parents. Bipolar Disorders 7, 344–350.Google Scholar
Hogstrom, LJ, Westlye, LT, Walhovd, KB and Fjell, AM (2013) The structure of the cerebral cortex across adult life: age-related patterns of surface area, thickness, and gyrification. Cerebral Cortex 23, 2521–2530.Google Scholar
Im, K, Lee, J-M, Lyttelton, O, Kim, SH, Evans, AC and Kim, SI (2008) Brain size and cortical structure in the adult human brain. Cerebral Cortex 18, 2181–2191.Google Scholar
Judd, LL, Akiskal, HS, Schettler, PJ, Endicott, J, Maser, J, Solomon, DA, Leon, AC, Rice, JA and Keller, MB (2002) The long-term natural history of the weekly symptomatic status of bipolar I disorder. Archives of General Psychiatry 59, 530–537.Google Scholar
Judd, LL, Schettler, PJ, Solomon, DA, Maser, JD, Coryell, W, Endicott, J and Akiskal, HS (2008) Psychosocial disability and work role function compared across the long-term course of bipolar I, bipolar II and unipolar major depressive disorders. Journal of Affective Disorders 108, 49–58.Google Scholar
Kaufman, J, Birmaher, B, Brent, D, Rao, U, Flynn, C, Moreci, P, Williamson, D and Ryan, N (1997) Schedule for affective disorders and schizophrenia for school-age children-present and lifetime version (K-SADS-PL): initial reliability and validity data. Journal of the American Academy of Child and Adolescent Psychiatry 36, 980–988.Google Scholar
Kempton, MJ, Haldane, M, Jogia, J, Grasby, PM, Collier, D and Frangou, S (2009) Dissociable brain structural changes associated with predisposition, resilience, and disease expression in bipolar disorder. Journal of Neuroscience 29, 10863–10868.Google Scholar
Konarski, JZ, McIntyre, RS, Kennedy, SH, Rafi-Tari, S, Soczynska, JK and Ketter, TA (2008) Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar Disorders 10, 1–37.Google Scholar
Ladouceur, CD, Almeida, JRC, Birmaher, B, Axelson, DA, Nau, S, Kalas, C, Monk, K, Kupfer, DJ and Phillips, ML (2008) Subcortical gray matter volume abnormalities in healthy bipolar offspring: potential neuroanatomical risk marker for bipolar disorder? Journal of the American Academy of Child and Adolescent Psychiatry 47, 532–539.Google Scholar
Lin, A, Ching, CRK, Vajdi, A, Sun, D, Jonas, RK, Jalbrzikowski, M, Kushan-Wells, L, Pacheco Hansen, L, Krikorian, E, Gutman, B, Dokoru, D, Helleman, G, Thompson, PM and Bearden, CE (2017) Mapping 22q11.2 gene dosage effects on brain morphometry. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience 37, 6183–6199.Google Scholar
Lin, K, Xu, G, Wong, NML, Wu, H, Li, T, Lu, W, Chen, K, Chen, X, Lai, B, Zhong, L, So, K and Lee, TMC (2015) A multi-dimensional and integrative approach to examining the high-risk and ultra-high-risk stages of bipolar disorder. EBioMedicine 2, 919–928.Google Scholar
Lopez de Lara, C, Jaitovich-Groisman, I, Cruceanu, C, Mamdani, F, Lebel, V, Yerko, V, Beck, A, Young, LT, Rouleau, G, Grof, P, Alda, M and Turecki, G (2010) Implication of synapse-related genes in bipolar disorder by linkage and gene expression analyses. The International Journal of Neuropsychopharmacology 13, 1397–1410.Google Scholar
Marsh, R, Zhu, H, Schultz, RT, Quackenbush, G, Royal, J, Skudlarski, P and Peterson, BS (2006) A developmental fMRI study of self-regulatory control. Human Brain Mapping 27, 848–863.Google Scholar
Matsuo, K, Kopecek, M, Nicoletti, MA, Hatch, JP, Watanabe, Y, Nery, FG, Zunta-Soares, G and Soares, JC (2012) New structural brain imaging endophenotype in bipolar disorder. Molecular Psychiatry 17, 412–420.Google Scholar
McGrath, BM, Wessels, PH, Bell, EC, Ulrich, M and Silverstone, PH (2004) Neurobiological findings in bipolar II disorder compared with findings in bipolar I disorder. The Canadian Journal of Psychiatry 49, 794–801.Google Scholar
McGuffin, P, Rijsdijk, F, Andrew, M, Sham, P, Katz, R and Cardno, A (2003) The heritability of bipolar affective disorder and the genetic relationship to unipolar depression. Archives of General Psychiatry 60, 497–502.Google Scholar
McIntosh, AM, Moorhead, TWJ, McKirdy, J, Hall, J, Sussmann, JED, Stanfield, AC, Harris, JM, Johnstone, EC and Lawrie, SM (2009) Prefrontal gyral folding and its cognitive correlates in bipolar disorder and schizophrenia. Acta Psychiatrica Scandinavica 119, 192–198.Google Scholar
Menon, V, Adleman, NE, White, CD, Glover, GH and Reiss, AL (2001) Error-related brain activation during a Go/NoGo response inhibition task. Human Brain Mapping 12, 131–143.Google Scholar
Moylan, S, Maes, M, Wray, NR and Berk, M (2013) The neuroprogressive nature of major depressive disorder: pathways to disease evolution and resistance, and therapeutic implications. Molecular Psychiatry 18, 595–606.Google Scholar
Nenadic, I, Maitra, R, Dietzek, M, Langbein, K, Smesny, S, Sauer, H and Gaser, C (2015) Prefrontal gyrification in psychotic bipolar I disorder vs. Schizophrenia. Journal of Affective Disorders 185, 104–107.Google Scholar
Ortiz, A, Bradler, K, Slaney, C, Garnham, J, Ruzickova, M, O'Donovan, C, Hajek, T and Alda, M (2011) An admixture analysis of the age at index episodes in bipolar disorder. Psychiatry Research 188, 34–39.Google Scholar
Panizzon, MS, Fennema-Notestine, C, Eyler, LT, Jernigan, TL, Prom-Wormley, E, Neale, M, Jacobson, K, Lyons, MJ, Grant, MD, Franz, CE, Xian, H, Tsuang, M, Fischl, B, Seidman, L, Dale, A and Kremen, WS (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cerebral Cortex 19, 2728–2735.Google Scholar
Pavlova, B, Perlis, RH, Alda, M and Uher, R (2015) Lifetime prevalence of anxiety disorders in people with bipolar disorder: a systematic review and meta-analysis. The Lancet Psychiatry 2, 710–717.Google Scholar
Rakic, P, Ayoub, AE, Breunig, JJ and Dominguez, MH (2009) Decision by division: making cortical maps. Trends in Neurosciences 32, 291–301.Google Scholar
Roberts, G, Green, MJ, Breakspear, M, McCormack, C, Frankland, A, Wright, A, Levy, F, Lenroot, R, Chan, HN and Mitchell, PB (2013) Reduced inferior frontal gyrus activation during response inhibition to emotional stimuli in youth at high risk of bipolar disorder. Biological Psychiatry 74, 55–61.Google Scholar
Roberts, G, Lenroot, R, Frankland, A, Yeung, PK, Gale, N, Wright, A, Lau, P, Levy, F, Wen, W and Mitchell, PB (2016) Abnormalities in left inferior frontal gyral thickness and parahippocampal gyral volume in young people at high genetic risk for bipolar disorder. Psychological Medicine 46, 2083–2096.Google Scholar
Roberts, G, Lord, A, Frankland, A, Wright, A, Lau, P, Levy, F, Lenroot, RK, Mitchell, PB and Breakspear, M (2017) Functional dysconnection of the inferior frontal gyrus in young people with bipolar disorder or at genetic high risk. Biological Psychiatry 81, 718–727.Google Scholar
Sarıçiçek, A, Yalın, N, Hıdıroğlu, C, Çavuşoğlu, B, Taş, C, Ceylan, D, Zorlu, N, Ada, E, Tunca, Z and Özerdem, A (2015) Neuroanatomical correlates of genetic risk for bipolar disorder: a voxel-based morphometry study in bipolar type I patients and healthy first degree relatives. Journal of Affective Disorders 186, 110–118.Google Scholar
Schaer, M, Cuadra, MB, Tamarit, L, Lazeyras, F, Eliez, S and Thiran, JP (2008) A surface-based approach to quantify local cortical gyrification. IEEE Transactions on Medical Imaging 27, 161–170.Google Scholar
Schaer, M and Eliez, S (2009) Contribution of structural brain imaging to our understanding of cortical development process. European Psychiatry Reviews 2, 13–16.Google Scholar
Selvaraj, S, Arnone, D, Job, D, Stanfield, A, Farrow, TF, Nugent, AC, Scherk, H, Gruber, O, Chen, X, Sachdev, PS, Dickstein, DP, Malhi, GS, Ha, TH, Ha, K, Phillips, ML and McIntosh, AM (2012) Grey matter differences in bipolar disorder: a meta-analysis of voxel-based morphometry studies. Bipolar Disorders 14, 135–145.Google Scholar
Silverstone, PH, Asghar, SJ, O'Donnell, T, Ulrich, M and Hanstock, CC (2004) Lithium and valproate protect against dextro-amphetamine induced brain choline concentration changes in bipolar disorder patients. The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry 5, 38–44.Google Scholar
Singh, MK, Kelley, RG, Howe, ME, Reiss, AL, Gotlib, IH and Chang, KD (2014) Reward processing in healthy offspring of parents with bipolar disorder. JAMA Psychiatry 71, 1148–1156.Google Scholar
Song, J, Bergen, SE, Kuja-Halkola, R, Larsson, H, Landén, M and Lichtenstein, P (2015) Bipolar disorder and its relation to major psychiatric disorders: a family-based study in the Swedish population. Bipolar Disorders 17, 184–193.Google Scholar
Sowell, ER, Peterson, BS, Thompson, PM, Welcome, SE, Henkenius, AL and Toga, AW (2003) Mapping cortical change across the human life span. Nature Neuroscience 6, 309–315.Google Scholar
Todd, RD, Reich, W, Petti, TA, Joshi, P, DePAULO, JR, Nurnberger, J and Reich, T (1996) Psychiatric diagnoses in the child and adolescent members of extended families identified through adult bipolar affective disorder probands. Journal of the American Academy of Child & Adolescent Psychiatry 35, 664–671.Google Scholar
Townsend, JD, Bookheimer, SY, Foland-Ross, LC, Moody, TD, Eisenberger, NI, Fischer, JS, Cohen, MS, Sugar, CA and Altshuler, LL (2012) Deficits in inferior frontal cortex activation in euthymic bipolar disorder patients during a response inhibition task. Bipolar Disorders 14, 442.Google Scholar
Wierenga, LM, Langen, M, Oranje, B and Durston, S (2014) Unique developmental trajectories of cortical thickness and surface area. NeuroImage 87, 120–126.Google Scholar
Winkler, AM, Kochunov, P, Blangero, J, Almasy, L, Zilles, K, Fox, PT, Duggirala, R and Glahn, DC (2010) Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. NeuroImage 53, 1135–1146.Google Scholar
Winsberg, ME, Sachs, N, Tate, DL, Adalsteinsson, E, Spielman, D and Ketter, TA (2000) Decreased dorsolateral prefrontal N-acetyl aspartate in bipolar disorder. Biological Psychiatry 47, 475–481.Google Scholar
Wise, T, Radua, J, Via, E, Cardoner, N, Abe, O, Adams, TM, Amico, F, Cheng, Y, Cole, JH, de Azevedo Marques Périco, C, Dickstein, DP, Farrow, TFD, Frodl, T, Wagner, G, Gotlib, IH, Gruber, O, Ham, BJ, Job, DE, Kempton, MJ, Kim, MJ, Koolschijn, PCMP, Malhi, GS, Mataix-Cols, D, McIntosh, AM, Nugent, AC, O'Brien, JT, Pezzoli, S, Phillips, ML, Sachdev, PS, Salvadore, G, Selvaraj, S, Stanfield, AC, Thomas, AJ, van Tol, MJ, van der Wee, NJA, Veltman, DJ, Young, AH, Fu, CH, Cleare, AJ and Arnone, D (2017) Common and distinct patterns of grey-matter volume alteration in major depression and bipolar disorder: evidence from voxel-based meta-analysis. Molecular Psychiatry 22, 1455–1463.Google Scholar
Wu, RH, O'Donnell, T, Ulrich, M, Asghar, SJ, Hanstock, CC and Silverstone, PH (2004) Brain choline concentrations may not be altered in euthymic bipolar disorder patients chronically treated with either lithium or sodium valproate. Annals of General Hospital Psychiatry 3, 13.Google Scholar
Yucel, K, McKinnon, MC, Taylor, VH, Macdonald, K, Alda, M, Young, LT and MacQueen, GM (2007) Bilateral hippocampal volume increases after long-term lithium treatment in patients with bipolar disorder: a longitudinal MRI study. Psychopharmacology 195, 357–367.Google Scholar
Figure 0

Table 1. Description of the participants

Figure 1

Fig. 1. Larger pars triangularis volume and surface area in affected familial and unaffected High-Risk groups relative to control participants. Means ± s.e.m. (a) Anatomical subdivisions of the inferior frontal gyrus (IFG). Significant group differences were localized to pars triangularis. (b) Volume of pars triangularis. (c) Surface area of pars triangularis.

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