BD offspring with ADHD and mood symptoms may be one group at particularly high risk for developing a full manic episode. While some studies have shown increased rates of ADHD among offspring of bipolar parents (for review, see DelBello et al., 2001; Chang et al., 2003), others have not (Hillegers et al., 2005; Duffy et al., 2010; Shaw et al., 2005; Birmaher 2010). Nevertheless, the clinical course of pediatric BD often begins in childhood with ADHD presentations (Tillman & Geller, 2006). Furthermore, parents with BD who also had ADHD as children may have younger age at onset of BD (Sachs et al., 2000) and may be more likely to already have a child with BD and ADHD (Chang et al., 2000). Thus, those BD offspring who do have ADHD may still be at high-risk for developing a subtype of early-onset BD that begins with an ADHD presentation (Carlson and Weintraub, 1993; Faraone et al., 1997). Studying the brain anatomy of these symptomatic BD offspring may shed light on neurobiological factors predicting development and progression of BD.

Magnetic resonance imaging (MRI) has been used in previous studies to examine various regional brain volumes in patients with BD, finding abnormalities in regions involved in mood regulation, including the amygdala, hippocampus, thalamus, caudate, and putamen (Frazier et al., 2005; Frazier et al., 2005). Recent meta-analyses have reported decreased whole-brain and prefrontal volumes, and increased globus pallidus and ventricular volumes in patients with BD compared to controls (Arnone et al., 2009). The meta-analysis by Kempton and colleagues (2008) also reported increased lateral ventricular enlargement, as well as increased rates of deep white matter hyperintensities. Both increased (Dewan et al., 1988; Dupont et al., 1995) and normal (Dolan et al., 1990; Strakowski et al., 1993; Caetano et al., 2001) thalamic volume has been reported in adults with BD. In adolescents with BD, there are reports of increased (Frazier et al., 2005), decreased (Dasari et al., 1999), and similar (Chang et al., 2005) thalamic volumes compared to controls. Hippocampal volumes in a group of adults and adolescents with BD were found to be smaller than those of healthy controls (Blumberg et al., 2003), while adults with BD have shown either unchanged (Altshuler et al., 1998; Hauser, Matochik et al., 2000) or decreased (Swayze et al., 1992) hippocampal volumes.

The amygdala has been one of the most prominent brain structures of interest in studies of BD, as it is highly involved in emotion processing and shows abnormal activation in functional imaging studies of adults with BD (Altshuler et al., 2005; Blumberg et al., 2005; Pavuluri et al., 2007). Morphometric abnormalities in the amygdala have also strengthened its likely role in the pathophysiology of BD (Dupont et al., 1995; Stoll et al., 2000; Strakowski et al., 2000; Blumberg et al., 2003; Chang et al., 2004; Blumberg et al., 2005; Chang et al., 2005; Strakowski et al., 2005; Garrett and Chang, 2008). Various research groups have reported increased (Strakowski et al., 1999; Altshuler et al., 2000), decreased (Pearlson et al., 1997), and similar (Swayze et al., 1992) amygdalar volumes in adults with BD. Findings in pediatric samples have been more consistent, as most, but not all, morphometric studies of children and adolescents with BD have found decreased amygdalar volumes compared with healthy controls (Blumberg et al., 2003; DelBello et al., 2004; Chang et al., 2005; Frazier et al., 2005; Pfeifer et al, 2008). Although the functional consequences of decreased volume are not known, a study of adults with unipolar depression found an inverse correlation between amydalar volume and activation during emotion processing (Siegle et al., 2003). Thus, decreased amygdalar volume in youth with BD might be associated with increased amygdala activation to an emotional stimulus. In fact, increased activation of the amygdala has been reported in pediatric BD patients (Rich et al., 2006). Taken together, these findings are consistent with theories of mood dysregulation in BD that posit limbic hyperactivity and prefrontal cortex hypoactivity to psychosocial stressors (Chang et al., 2004; Blumberg et al., 2005).

Oral and written consent from the parents as well as oral and written assent from the youth were obtained, and both the parents and the offspring were interviewed. At least one parent had BD I or II diagnosed by the Structured Clinical Interview for DSM-IV Axis I disorders (SCID) (First MB 1995), administered by a trained master's degree level clinician and/or a psychiatrist board certified in general psychiatry. Both parents were interviewed, and on occasion, the non-bipolar parent had an Axis I diagnosis within the high-risk group. For inclusion in the high-risk BD group, in addition to parental diagnosis of BD, all children met criteria for ADHD and had at least moderate mood symptoms, as indicated by a score of >10 on the Young Mania Rating Scale (YMRS, (Fristad et al., 1995)) or a score of >30 on the Children's Depressive Rating Scale – Revised (CDRS-R, (Poznanski et al., 1985)). Subjects could have depression or dysthymia, but not cyclothymia, bipolar I, or bipolar II disorder. Subjects were allowed to meet bipolar disorder, not otherwise specified (BD-NOS) criteria, defined as having either one less criterion B symptom than necessary for a (hypo)-manic episode, or enough criterion B symptoms but only a 2–3 day duration of the episode. All subjects, patients and healthy volunteers, were evaluated by the affective disorders module of the Washington University in St. Louis Kiddie Schedule for Affective Disorders and Schizophrenia (WASH-U-KSADS) (Geller et al., 1996; Geller et al., 2001) and the Schedule for Affective Disorders and Schizophrenia for School-Age Children, Present and Lifetime (K-SADS-PL) (Kaufman et al., 1997). Researchers with at least a Masters degree and two years of clinical experience administered the KSADS-PL and WASH-U-KSADS. Diagnostic decisions were ultimately made by a board-certified child psychiatrist who reviewed the interview, and also performed a clinical interview on the child to confirm the diagnoses. . Inter-rater reliability was established at the outset by rating videotaped interviews, observing trained rater interviews, and performing interviews with observation by a trained rater, as described by Geller et al. (1998) (four consecutive patients with 100% agreement on diagnoses). The inter-rater reliability for diagnoses was a kappa of > 0.9. Current and lifetime diagnoses were established according to DSM-IV criteria.

Bipolar offspring had psychostimulants discontinued for at least 24 hours before the scan, primarily due to a concurrent functional MRI study of attention. They were allowed to continue any other current medications such as mood stabilizers or antidepressants due to the risk of mood destabilization. Medication history was obtained from interviews with subjects and parents and review of medical records when available. Past exposure to lithium or valproate was recorded if the subject had at least 6 months treatment of either agent at standard doses or serum levels.

For inclusion in the control group, healthy volunteers could not have a current or lifetime DSM-IV psychiatric diagnosis, neither parent had a lifetime or current psychiatric diagnosis by SCID, and the participant did not have a first or second degree relative with BD as determined by the Family History Research Diagnostic Criteria (Andreasen et al., 1977). None of the healthy control group parents had any Axis I diagnoses.

All the study participants and healthy volunteers were scanned on a GE 3Tesla scanner. Coronal 3D volumetric spoiled gradient echo (SPGR) series were obtained with the following parameters: TR = 35, TE = 6, flip angle = 45, slice thickness = 1.5 or 1.6 mm, and matrix = 256×192 for 124 slices. The number of scans using a 1.6 mm rather than a 1.5 mm slice thickness was not different between the at-risk and control groups (at-risk=5 of 22 scans; control=3 of 22 scans; chi-square(1)=.611, p=.43.)

The volumetric analysis was performed using BrainImageJava software v. 0.13.4 ((Reiss, 2002), Center for Interdisciplinary Brain Sciences Research, CIBSR; http://cibsr.stanford.edu) for semi-automated image processing and quantification.

Image processing included removal of non-brain tissue, correction of non-uniformity, and positional normalization to anterior and posterior commissures in a stereotactic space (Talairach and Tournoux, 1988). Each brain was divided into lobes with a semi-automated stereotactic-based parcellation method (Kates et al., 1999), based on the raters' identification of the anterior commissure, the posterior commissure, and a midsagittal point above the axis created by the first two points. Raters who conducted morphometric analyses were blind to the diagnosis of each subject. Voxels comprising brain tissue were then segmented into gray matter, white matter, and cerebrospinal fluid (CSF) using a semi-automated fuzzy tissue segmentation algorithm (Reiss et al., 1998). The total brain volume (TBV) was calculated as the sum of all brain regions. Total cerebral volume was calculated by adding cerebral total tissue with cortical and ventricular CSF. Total brain tissue was calculated by adding cerebral total tissue, cerebellar tissue, and brainstem tissue.

Subcortical regions were outlined manually by a rater who had demonstrated reliability with gold standard tracings established with an independent rater and dataset (single measure intraclass correlation coefficient) > 0.9). Regions were drawn on positionally normalized brain image stacks in the coronal orientation. Hippocampi were traced starting at the slice where a clear distinction between amygdala and hippocampus was first visible and outlined proceeding posteriorly until the structure disappeared. The superior white matter tract extending from the temporal lobe was used as an inferior border of the hippocampus, medial border was defined by CSF and by the pons, where present, and the lateral border was marked by CSF or white matter tracts on the lateral edge of the hippocampus.

Thalami were traced starting on the slice where the structure was first visible and followed until thalamic gray matter disappeared; the border between the gray matter of the thalamus and the surrounding white matter was used to outline the thalamus.

Amygdalae were traced starting on the slice demonstrating the thickest extent of the anterior commissure and following the structure towards the posterior end of the brain. The most superior white matter tract extending from the temporal lobe marked the inferior border, CSF marked the medial border, endorhinal sulcus marked the superior border, and a thick, central white matter tract of the temporal lobe was used as the lateral border of amygdala (Figure 1).

Covarying for age and TBV, total amygdalar volume in high-risk subjects was not significantly different from the amygdalar volume of the healthy controls (4.640 ± 0.607 cm³versus 4.641 ± 0.567 cm³, F (1, 40) = 0.409, p = 0.53), with neither the right amygdala (2.270 ± 0.328 cm³ versus 2.293 ± 0.274 cm³, F (1, 40) = 0.024, p = 0.88) nor the left amygdala significantly different in volume (2.368 ± 0.341 cm³ versus 2.347 ± 0.341 cm³, F (1, 40) = 0.982, p = 0.33), (see table 2 for unadjusted means and error bars). The effect size (Cohen's d) for the difference in left amygdala tissue volume was 0.062, for the right amygdala tissue 0.076, and for the total amygdala tissue 0.002. We also analyzed our data without the age covariate and these findings did not change. There was no significant effect of gender on any amygdala or hippocampus measure. The interaction between gender and diagnosis was also not significant.

In further exploratory analyses, there was no significant difference in thalamic volume (15.544 ± 1.264 cm³ versus 16.088 ± 1.568 cm³, F (1, 40) = 0.99, p = 0.32) or hippocampal volume in high-risk subjects compared with healthy controls (6.847 ± 0.721 cm³ versus 7.256 ± 0.837 cm³, F (1, 40) = 1.973, p = 0.17) (Table 2). The effect size (Cohen's d) for the difference in total thalamic volume was 0.382, and for the difference in total hippocampal volume was 0.524.

The current study aimed to address this question by examining children at high risk for BD development by virtue of their genetic susceptibility and symptoms of ADHD and mood dysregulation (Chang et al., 2004; Blumberg et al., 2005). We hypothesized that these children would have decreased amygdalar volumes compared with healthy controls, indicating a neuropathology that might be partially responsible for their mood difficulties and increased risk for full mania. As we found no significant difference between the amygdalar volumes of the BD offspring and healthy controls, our hypotheses were not supported. Thus, amygdalar volumes may be reduced compared with healthy controls only after a full manic episode and/or more prolonged illness. However, chronicity may produce different effects on amygdala volumes depending on age, as some studies have reported larger amygdala volumes in more heterogeneous samples of adults with BD (Strakowski et al., 1999; Altshuler et al., 2000), a finding supported by meta-analyses (Pfiefer et al., 2008; Hajek et al., 2009b). These results in adults with BD may also be due to external factors, such as substance abuse or, more likely, exposure to medications such as lithium (Chang et al., 2005; Foland et al., 2008).

Regardless, abnormalities of amygdalar structure may not predate a full bipolar diagnosis. To confirm this impression, longitudinal follow-up of these subjects to determine whether they do indeed develop fully characterized BD is necessary. Also, we found no significant thalamic or hippocampal volume differences between the BD offspring and the healthy controls. These findings are consistent with our earlier study of BD adolescents (Chang et al., 2005). However, hippocampal volume comparisons revealed a moderate effect size of 0.52, indicating that 93 subjects and controls would be needed at a power of 0.80 to reveal a statistically significant decrease in hippocampal volume in high-risk offspring. To detect decreased thalamic volume, 172 subjects would be needed. The effect size of total amygdalar volume difference (.002) was too low to even perform a meaningful power calculation to determine sample size needed to detect differences between groups. To our knowledge, ours is the first study to find no amygdalar, thalamus, or hippocampal volume differences in a pediatric cohort of symptomatic bipolar offspring who are high-risk for BD.

Both animal and human studies have established that the amygdala plays an important role in emotion-related processing (Garrett and Chang, 2008). The amygdala has been shown to be involved in the expression and acquisition of fear conditioning (Wilensky et al., 2006) and emotional responses to threatening stimuli (Izquierdo et al., 2005). Humans with bilateral amygdala damage have impaired judgment of emotional facial expressions (Adolphs and Tranel, 2004), and humans with unilateral or bilateral amygdala damage have impaired recognition of social emotions (Adolphs et al., 2002). Thus, abnormal amygdalar volume may indicate abnormal emotion perception and regulation.

BD offspring who are experiencing significant mood symptoms, and especially those already with ADHD, may have a prodromal form of BD, and, if untreated, may develop full BD. For example, in a large prospective naturalistic study of BD in children and adolescents, 25% of children with subsyndromal BD experienced a full manic or hypomanic episode within 24 months (Birmaher et al., 2006). Similarly, early childhood ADHD may represent a risk factor for BD development, as 28% of 81 children with ADHD developed BD over 6 years (Tillman and Geller, 2006). While some studies have not shown increased rates of ADHD among offspring of bipolar parents (Hillegers et al., 2005; Duffy et al., 2010; Shaw et al., 2005), others found that children with ADHD and a first degree relative with BD appear to be at high-risk for BD development, although the exact risk is unknown (Carlson and Weintraub, 1993; Faraone et al., 1997). Thus, while we cannot be certain that all of our high-risk subjects will develop DSM-IV bipolar I or II disorder, they do represent a population at fairly high risk for a full manic episode.

A possible explanation for our negative findings is that not all of our high-risk subjects will develop full mania. Thus, a subset of our cohort may develop mania, while the rest will not. Other studies of bipolar offspring have also reported normal amygdalar volumes (Ladouceur et al., 2008; Singh et al., 2008; Hajek et al., 2009a). However, these studies differed significantly from ours. First, Singh and colleagues (2008) studied 21 offspring who had various levels of psychopathology, ranging from healthy (24%), to ADHD (19%) to non-bipolar mood disorders such as dysthymia and depression (43%). Subjects were also younger than those in our study (mean age 9.7 years). Thus, this sample represented a younger, more heterogeneous and potentially less ill group than our sample. Ladouceur and colleagues studied 20 healthy offspring of parents with BD, none having a current DSM-IV diagnosis, with a mean age of 13 years. These researchers also used voxel-based morphometry and did not perform manual tracings on regions including the amygdala. Thus, this sample, being fairly old and free of psychopathology, might be considered a particularly low-risk “at-risk” group and may even be displaying neurobiological markers of resilience, such as the reported finding of increased volume of the left hippocampal gyrus (Ladouceur et al., 2008). The study by Hajek and colleagues (2009a) included a wider age range of at-risk subjects, from ages 15 to 30, included offspring of parents with MDD (but with a second-degree relative with BD), and their affected high-risk group included subjects with MDD as well as subjects with bipolar I or II disorder. Again, our sample represented a symptomatic, relatively homogenous group of bipolar offspring with ADHD and at least moderate symptoms of mania and depression, with 36% meeting BD NOS criteria. These children are arguably closer to a full manic episode (Birmaher et al., 2009), and thus one might expect to find decreased amygdalar volume in this cohort, which was not the case.

One significant limitation of our study is that all of the subjects in the high-risk group had ADHD, the presence of which might have an impact on our findings. While some studies indicate possible amygdalar and hippocampal abnormalities in the ADHD populations (Plessen et al., 2006; Brotman et al., 2010), others do not implicate the amygdala (Bush et al., 2005). A future study with a comparison group of non-bipolar offspring with ADHD would provide further insight into this issue. Nine of the subjects in the current study had anxiety and/or depression diagnoses. While there have been reports of amygdala abnormalities in populations with anxiety and/or depression, we did not find a significant difference in amygdala volume between these subjects and the other bipolar offspring, although it should be noted that these comparisons were underpowered and should be considered preliminary. Also, we studied only one subset of children likely to be prodromal for BD. Other studies should be conducted on BD offspring with unipolar depression and children with other forms of BD NOS.

The fact that some of our subjects were likely to be prepubertal while others are postpubertal is a possible confounding factor, as pubertal state may influence amygdalar volume (Kraemer et al., 2000). The subjects' medication exposure is another potentially confounding variable: 3 of our subjects had been exposed to lithium, 9 to valproate, and 4 to antipsychotics. While it has been postulated that such medications may increase cortical and subcortical gray matter volumes (Pfeifer et al., 2008), amygdalar volume was not statistically different between the participants with and without lithium or valproate exposure. However, psychotropic medications clearly affect brain function and structure, and may have had an effect on our results that was not detected because of the small sample size.