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. Author manuscript; available in PMC: 2024 Nov 1.
Published in final edited form as: J Affect Disord. 2023 Aug 8;340:269–279. doi: 10.1016/j.jad.2023.08.018

Cerebellar Morphological Differences and Associations with Extrinsic Factors in Bipolar Disorder Type I

Gail I S Harmata 1,2,7, Ercole John Barsotti 1,3, Lucas G Casten 1,4, Jess G Fiedorowicz 1,2,14, Aislinn Williams 1,2, Joseph J Shaffer 1,2,7,13, Jenny Gringer Richards 7, Leela Sathyaputri 7, Samantha L Schmitz 1, Gary E Christensen 5,6, Jeffrey D Long 1,9, Marie E Gaine 1,2,10, Jia Xu 7, Jake J Michaelson 1,2,4, John A Wemmie 1,2,11,12,15, Vincent A Magnotta 1,2,7,8
PMCID: PMC10529949  NIHMSID: NIHMS1924098  PMID: 37562560

Abstract

Background.

The neural underpinnings of bipolar disorder (BD) remain poorly understood. The cerebellum is ideally positioned to modulate emotional regulation circuitry yet has been understudied in BD. Literature suggests differences in cerebellar activity and metabolism in BD, however findings on structural differences remain contradictory. Potential reasons include combining BD subtypes, small sample sizes, and potential moderators such as genetics, adverse childhood experiences (ACEs), and pharmacotherapy.

Methods.

We collected 3T MRI scans from participants with (N = 131) and without (N = 81) BD type I, as well as blood and questionnaires. We assessed differences in cerebellar volumes and explored potentially influential factors.

Results.

The cerebellar cortex was smaller bilaterally in participants with BD. Polygenic propensity score did not predict any cerebellar volumes, suggesting that non-genetic factors may have greater influence on the cerebellar volume difference we observed in BD. Proportionate cerebellar white matter volumes appeared larger with more ACEs, but this may result from reduced ICV. Time from onset and symptom burden were not associated with cerebellar volumes. Finally, taking sedatives was associated with larger cerebellar white matter and non-significantly larger cortical volume.

Limitations.

This study was cross-sectional, limiting interpretation of possible mechanisms. Most of our participants were White, which could limit the generalizability. Additionally, we did not account for potential polypharmacy interactions.

Conclusions.

These findings suggest that external factors, such as sedatives and childhood experiences, may influence cerebellum structure in BD and may mask underlying differences. Accounting for such variables may be critical for consistent findings in future studies.

Keywords: Bipolar Disorder, Cerebellum, Cerebellar Volume, Mood Disorders

Introduction

Bipolar disorder type I is a chronic condition characterized by mood instability, affecting approximately 1% of the U.S. population (Clemente et al., 2015). Bipolar disorder is often debilitating, disrupting personal, work-related, and social function (Alonso et al., 2011). Despite its seriousness, the cause of bipolar disorder remains unknown. Numerous brain imaging studies have been conducted to gain insight into the underlying neurobiology by examining brain morphology, function, and metabolism. Although findings vary regarding morphological changes associated with bipolar disorder, several meta- and mega-analyses have observed reduced overall cerebral volume and increased ventricle size in bipolar disorder along with more specific volume changes of the prefrontal cortex, cingulate cortex, temporal lobe, and subcortical structures such as the striatum and amygdala (de Zwarte et al., 2019; Hallahan et al., 2011; Houenou et al., 2011; Wise et al., 2017). Functional differences have similarly been observed in overlapping brain regions (Chen et al., 2022). These identified areas are consistent with a model of dysregulated emotional control in bipolar disorder (Strakowski et al., 2012). However, the nature of such dysregulation is not precisely understood.

One brain area of potential importance to emotional dysregulation in bipolar disorder is the cerebellum. Although often associated with coordination and motor control, the cerebellum has been increasingly recognized to contribute to cognitive functioning and possibly psychiatric disorders (Phillips et al., 2015; Schmahmann, 2019). The cerebellum has connections to nodes of the emotional control network, including the amygdala and prefrontal cortex (Blatt et al., 2013). Several reports have implicated the cerebellum in bipolar disorders. Studies have observed altered metabolism and neurochemistry (Cecil et al., 2003; Lai et al., 2019; Magnotta et al., 2022; Wu et al., 2021); differences in T1ρ relaxation times, potentially reflecting pH differences (Johnson et al., 2018; Johnson et al., 2015); and altered functional connectivity (Cui et al., 2022; Shinn et al., 2017; Saleem et al., 2023). Furthermore, cerebellar activity has been linked to mood state in participants with bipolar disorder (Fleck et al., 2011; Shaffer et al., 2018), supporting a potential model of cerebellar involvement in mood regulation.

Findings from structural imaging studies of the cerebellum in bipolar disorder have been inconsistent. While many studies have found the cerebellum or regions thereof (e.g., hemispheric gray matter, vermal subregions, etc.) to be smaller in bipolar disorder compared to controls (Baldaçara et al., 2011; Berk et al., 2017; de Zwarte et al., 2019; Kim et al., 2013; Lin et al., 2018; Lisy et al., 2011; Moorhead et al., 2007; Rimol et al., 2010; Sarıçiçek et al., 2015), other studies have found the opposite (Adler et al., 2007; Cui et al., 2011; Wise et al., 2017) or no differences (Demirgören et al., 2019; Goikolea et al., 2018; Laidi et al., 2015; McDonald et al., 2005). One possible explanation for some of these contradictory results may be explained by differences in sampling time points: some have found smaller or non-significantly smaller cerebellar volumes in bipolar disorder associated with age or number of mood episodes (DelBello et al., 1999; Lisy et al., 2011; Monkul et al., 2008; Moorhead et al., 2007; Yates, 1987). This could indicate that the cerebellum experiences accelerated atrophy with the disorder. Furthermore, one study found greater reduction in cerebellar vermis gray matter density when accounting for medication status (Kim et al., 2013), while another study reported larger cerebellar cortex and white matter volumes with lithium treatment (Jones et al., 2022); thus, medication effects make interpretation of findings in bipolar disorder challenging. Genetic loading for bipolar disorder may also influence cerebellar volumes. For example, a prior study found that participants with bipolar disorder and a family history of mood disorders had smaller volumes than participants with bipolar disorder without a family history (Brambilla et al., 2001). Another study found that both participants with bipolar disorder and their first-degree relatives had smaller cerebellar volumes than controls (Sarıçiçek et al., 2015; but see de Zwarte et al., 2019). Finally, many bipolar disorder studies have small sample sizes, and while most of these studies examined bipolar disorder type I only, several combined bipolar disorder types I and II, including the largest study (de Zwarte et al., 2019). Some or all of these factors may have contributed to the heterogeneity of the results that have been reported in the literature.

Potential moderating factors of cerebellar morphology could also include environmental variables such as adverse childhood experiences (ACEs). ACEs are known to be more likely in bipolar disorder (Palmier-Claus et al., 2016), and having childhood maltreatment has been shown to be related to outcomes such as earlier age of onset, increased symptom severity, and elevated suicide attempt risk (Agnew-Blais et al., 2016). As ACEs can occur at any point in childhood, they may impact multiple neurodevelopmental stages, including important periods of brain maturation and pruning. Thus, it is unsurprising that greater number of ACEs has been linked to changes in brain morphology in bipolar disorder, including reduced orbitofrontal gray matter (Poletti et al., 2016) and differences in diffusion weighted imaging measures (Benedetti et al., 2014). However, we have been unable to find a paper that specifically examined ACEs and the cerebellum in bipolar disorder, thus representing a gap in the literature.

The lack of consensus and literature gaps make understanding cerebellar involvement in bipolar disorder challenging. To address this issue, we examined cerebellar morphology in a large single-site sample composed of participants with bipolar disorder type I (N = 131) and a comparison control sample (N = 81). We hypothesized that we would find significantly smaller cerebellar volumes in participants with bipolar disorder compared to controls, consistent with the largest multi-site study (de Zwarte et al., 2019). We also evaluated whether potentially contributing variables such as genetic loading, adverse childhood experiences, time from mood symptom onset, self-reported symptom burden, and medication status would correlate with cerebellar volumes.

Methods

Participants.

Following University of Iowa Institutional Review Board approval, individuals with bipolar disorder type I and control participants were recruited to participate into a neuroimaging and genetics study (Figure 1) after providing written informed consent. All possible participants were screened for exclusion criteria, including neurological comorbidities, current drug or alcohol abuse, prior loss of consciousness for more than 10 minutes, or MRI contraindications. Current drug or alcohol abuse was an exclusion criterion because we anticipated this would affect functional connectivity data, which we also analyzed separately (Saleem et al., 2023). A mid-study exclusion criterion for controls currently taking a medication for depression, schizophrenia, or bipolar disorder was applied retroactively, which excluded one previously collected control participant from the analyses who was receiving active treatment for depression. Controls were recruited in an attempt to frequency-match to participants with bipolar disorder type I (referred to as bipolar disorder in the remainder of the text) based on age, sex, race, and subjective social economic status from self-report on the MacArthur Scale of Subjective Social Status, on which participants identify their relative standing on a 10-rung ladder (Adler et al., 2000). Participants were interviewed and participants with bipolar disorder had their diagnosis confirmed via the Structured Clinical Interview for DSM Disorders (SCID). We collected the Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania Rating Scale (YMRS), the Beck Anxiety Inventory (BAI), parental psychiatric history, and current medications. Psychiatric medications were sorted into classes modified from the WHO Anatomical Therapeutic Chemical system (WHO Collaborating Centre for Drug Statistics Methodology, 2022): antidepressants, antipsychotics, sedatives, anticonvulsants, stimulants, and lithium. We also collected a 10-item Adverse Childhood Experiences (ACEs) Questionnaire (based on Felitti et al., 1998), which covers experience of abuse (psychological, physical, and sexual), and household dysfunction (lack of love or support, lack of provision or parental neglect, parental divorce or separation, physical abuse of maternal figure, household substance abuse, household mental illness, and household member going to prison). Participants with bipolar disorder were asked to retrospectively report estimated symptom burden over the course of the last decade (or since diagnosis, if less than 10 years prior), an approach used previously (Sodhi et al., 2012). Finally, most participants had a blood sample taken for genetic analysis.

Figure 1.

Figure 1.

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Study Schematic. (A) We recruited 150 participants with bipolar disorder type I and 90 control participants. After excluding screen failures and participants that were unable to complete the 3T anatomical scan, we obtained cerebellar volumetric data from most participants (81 control participants, 130 bipolar participants). During the site visit, we also collected blood samples. As of the time of this study, 64 control participants and 103 bipolar participants had both anatomical scans and polygenic risk scores calculated from the blood sample analysis. (B) Coronal and sagittal views of cerebellar segmentation used for most analyses in this report. This segmentation resulted from a custom atlas based on the Neuromorphometrics segmentation (Neuromorphometrics Inc., 2007).

3T MR Imaging Protocol.

Imaging was conducted on either a GE 750W or Premier 3T scanner with a 32 channel or 48 channel head coil, respectively. The same imaging protocol was acquired across both scanner configurations, which included the following anatomical imaging scans: 3D coronal T1-weighted BRAVO and 3D sagittal T2-weighted CUBE (see parameters in Saleem et al., 2023). Additional MR scans were also collected including resting state fMRI but were not used in this study.

MRI Image Analysis.

The T1- and T2-weighted scans described above were analyzed using BRAINS AutoWorkup (Forbes et al., 2016; Ghayoor et al., 2018; Kim et al., 2015; Kim et al., 2016), which includes the following steps: 1) AC-PC alignment; 2) bias field correction; 3) tissue classification; and 4) anatomical labeling using a custom atlas based upon the Neuromorpometrics segmentation (Neuromorphometrics Inc., 2007). This atlas includes parcellation of the cerebral cortex, white matter, subcortical structures, brainstem, and cerebellum. The cerebellum was divided into gray and white matter as well as vermis lobules (I-V, VI-VII, and VIII-X) (Figure 1B). All subdural tissue components were combined to estimate intracranial volume. Regional volumetric data was divided by intracranial volume to control for differences in cranial size. This resulted in proportional volumetric data. We focused on the cerebellar regions of interest which included left/right cerebellar gray matter, left/right cerebellar white matter, and three cerebellar vermis lobules (I-V, VI-VII, and VIII-X). To explore cerebellar volume differences in more detail, we also parcellated the cerebellum using the SUIT atlas (Diedrichsen et al., 2009) by non-linear registration of the MNI atlas to each participant’s using ANTS (Avants et al., 2008).

SNP genotyping and quality control.

Available samples were genotyped with Illumina’s Infinium PsychArray-24 v1.3 BeadChip (N = 188), genotypes were called using GenomeStudio2.0 software with Illumina’s provided cluster files. PLINK (Purcell et al., 2007) was used for quality control. Data was filtered down to SNPs, removing indels and non-biallelic sites. SNPs with low minor allele frequency in our sample (< 5%) were removed. All samples had high genotyping rates (> 99%). SNPs defying Hardy-Weinberg equilibrium were removed (p-value < 1 × 10−6). Four individuals were removed due to relatedness (identity-by-descent cutoff of 0.125), 8 were removed due to high heterozygosity rates, and 6 more were removed due to population structure (i.e., did not cluster with the European superpopulation in 1000 Genomes (1000 Genomes Project Consortium et al., 2015)).

The final sample size for the genetic analysis was 170, with data available from 104 participants with bipolar disorder and 66 controls. Data for the 170 individuals with 350,345 SNPs passing quality control were then imputed to the HRC r1.1 hg19 build reference dataset (Loh et al., 2016) using Michigan Imputation Server MiniMac4 (Das et al., 2016). SNPs with imputation R2 scores < 0.3 were dropped. Imputed genotype dosages were converted to hard calls using a cutoff of 90% probability. Finally, SNPs were subset to HapMap3 SNPs (International HapMap 3 Consortium et al., 2010) for subsequent polygenic propensity score calculations (final number of SNPs = 1,053,095).

Polygenic Propensity Scores (PGS).

A polygenic propensity score is the weighted sum of genetic risk variants, providing a score that represents an individual’s genome-wide risk for a trait. These scores focus on common genetic variants, which are found in at least 1% of the population. Polygenic propensity scores have shown significant predictive accuracy in disease and quantitative trait predictions (Manikpurage et al., 2021; Okbay et al., 2022). The risk genetic variants are identified and weighted in previously published Genome Wide Association Studies (GWAS) of biobank-scale cohorts. For this study, GWAS summary statistics from the most recent PGC bipolar disorder study (Mullins et al., 2021) were used to compute PGS for bipolar disorder in the 170 samples with the quality-controlled genotype and neuroimaging data. PGS were calculated for our cohort using LDpred2 (Privé et al., 2020) implemented by the “bigsnpr” package (Privé et al., 2018) in R (R Core Team, 2021). For linkage disequilibrium reference, a dataset of 362,320 European individuals of the UK Biobank (provided by the developers of LDpred2) was used for calculating the genetic correlation matrix, estimating heritability, and calculating infinitesimal beta weights. PGS were then corrected for population stratification and z-scaled by sex. Of the 170 participants with a calculated PGS, 166 participants (N = 103 cases and N = 64 controls) had available neuroimaging data and met all other inclusion criteria for this report as described above.

Statistical Analysis.

Analysis was performed in R version 4.1.1 (R Core Team, 2021) and RStudio (RStudio Team, 2020) using the following packages: car (Fox and Weisberg, 2019), arsenal (Heinzen et al., 2021), emmeans (Lenth, 2022), tidyverse (Wickham et al., 2019), rstatix (Kassambara, 2021), and ggprism (Dawson, 2022). We tested cerebellar white matter, cerebellar gray matter, and vermal volumes separately using linear regression models to assess whether bipolar diagnosis significantly predicted volume. Next, we tested the effects of genetic loading for bipolar disorder on cerebellar volumes while employing the same covariates by replacing diagnosis in the linear models with PGS for bipolar disorder. We followed these analyses by evaluating the data only from the participants with bipolar disorder to determine if other demographic factors predicted cerebellar volumes. For the medication follow-up analysis, we used ANCOVA models with Tukey-corrected pairwise comparisons to assess the effect of the following groupings: control participants not on the medication class, Ctrl (−); participants with bipolar disorder not on the medication class, BD (−); and participants with bipolar disorder on the medication class, BD (+). To adjust for effects of age in years (linear effect), sex (categorical effect), and potential scanner differences (categorical effect), we included these as covariates in all linear regression and ANCOVA models. To correct for testing multiple cerebellar regions, we utilized false discovery rate (FDR) correction; the resulting q-values were considered significant if q < 0.05. Demographic differences (Table 1) were assessed by t-test or chi-square test as appropriate.

Table 1.

Demographics for control participants (Ctrl, N = 81) and bipolar type I (N = 131) participants (BD).

Variable Ctrl (N=81) BD (N=131) p value
Biological Sex (F, M) (53, 28) (85, 46) 0.935
Age (years) Mean (SD) 40.5 (14.3) 39.8 (13.4) 0.735
BMI Mean (SD) 27.2 (5.2) 31.0 (7.8) < 0.001*
Educational Attainment Mean (SD) 15.5 (2.1) 14.9 (2.2) 0.059
Self-report SES Mean (SD) 6.3 (1.3) 5.2 (1.9) < 0.001*
N missing 1 1
Intracranial Volume (cm3) Mean (SD) 1460.324 (159.042) 1477.179 (183.717) 0.496
Any parental psych N Yes (% Yes) 30 (39.0%) 92 (78.0%) < 0.001*
illness N “Unknown” or missing 4 13
MADRS Score Mean (SD) 2.8 (4.5) 14.5 (9.4) < 0.001*
Range 0 – 23 0 – 44
YMRS Score Mean (SD) 1.0 (1.7) 6.7 (7.0) < 0.001*
Range 0 – 9 0 – 40
N missing or incomplete 0 1
BAI Score Mean (SD) 3.0 (5.0) 13.4 (11.4) < 0.001*
N missing or incomplete 0 5
Range 0 – 27 0 – 45
ACE Score Mean (SD) 1.5 (1.8) 3.7 (2.7) < 0.001*
N missing or incomplete 19 34
Medication Status (BD)
Antidepressant(s) N Yes (% Yes) 76 (58.0%)
Antipsychotic(s) N Yes (% Yes) 73 (55.7%)
Sedative(s) N Yes (% Yes) 56 (42.7%)
Anticonvulsant(s) N Yes (% Yes) 56 (42.7%)
Stimulant(s) N Yes (% Yes) 13 (9.9%)
Lithium N Yes (% Yes) 36 (27.5%)
Time from first mood (years) (BD)
Mean (SD) 18.4 (13.2)
Range 0.0 – 46.0
% time euthymic (BD) Mean (SD) 42.3 (28.8)
Range 0 – 98%
N missing 2
% time depressed (BD) Mean (SD) 38.9 (25.3)
Range 0 – 90%
N missing 2
% time manic (BD) Mean (SD) 18.9 (17.3)
Range 0 – 95%
N missing 2
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Significant differences are indicated (p < 0.05, *).

Results

Demographics.

Study demographics are shown in Table 1. The control and bipolar disorder groups were not significantly different in age, sex, or intracranial volume (all p > 0.05). However, participants with bipolar disorder had a non-significantly lower educational attainment (p = 0.059), and participants with bipolar disorder had significantly lower perceived socioeconomic status than controls (5.2 vs. 6.3, p < 0.001) and a higher BMI (31.0 vs. 27.2 kg/m2, p < 0.001). In addition, participants with bipolar disorder more frequently reported a parental history of psychiatric illness (78.0% vs. 39.0%, p < 0.001) and a greater number of adverse childhood experiences (3.7 vs. 1.5 ACEs; p < 0.001) than controls. It is important to note that household mental illness is considered an ACE and may contribute to other factors included in the overall ACE score, such as parental separation or drug/alcohol abuse in the household. Finally, participants with bipolar disorder had significantly higher depression, mania, and anxiety ratings (all p < 0.001).

Within the participants with bipolar disorder, the sample included a wide range of time from first mood episode (0 to 46 years), with an average of 18.4 years since onset. Similarly, we were able to capture a variety of self-reported symptom burdens, with ranges of 0% to more than 90% of time spent in each mood state over the past decade (or since diagnosis if <10 years ago). On average, participants with bipolar disorder spent a similar amount of time euthymic and depressed states (42.3% and 38.9% respectively), with a lower amount of time in a manic state (18.9%).

Differences in cerebellar volume in bipolar disorder versus controls.

To investigate differences in cerebellar volume in bipolar disorder, we conducted MRI imaging and analyzed the resulting cerebellar data, normalized to intracranial volume (ICV), using linear regression controlling for age, sex, and scanner (Table 2). We found that participants with bipolar disorder had smaller cerebellar gray matter volumes compared to controls with estimated mean differences of −2.053 cm3 (p = 0.001, q = 0.010) and −1.880 cm3 (p = 0.005, q = 0.019) for left and right hemispheres, respectively. We did not detect significant differences in cerebellar white matter volumes nor vermal volumes (all p > 0.05 and all q > 0.05), but all cerebellar regions tended to be smaller in participants with bipolar disorder. To determine whether the observed differences in cerebellar gray matter were driven by a specific region, we used the SUIT atlas (Diedrichsen et al., 2009) to parcellate the cerebellum and explored all resulting volumes. Numerous hemispheric regions were significantly or non-significantly smaller in bipolar disorder (Supplemental Table 1, Supplemental Figure 1), suggesting differences in cerebellar volume are diffuse throughout much of the cerebellum.

Table 2.

Cerebellar cortex volumes are smaller bilaterally in bipolar disorder type I.

Cerebellar Volume (proportion of ICV) Effect of bipolar diagnosis (N “Ctrl” = 81, N “BD” = 131)
Beta Coefficient Estimate (SE) Estimated Group Difference (converted to cm3) p-value q-value
Left Cerebellar Cortex −1.396e-03 (4.313e-04) −2.053 0.001 0.010*
Right Cerebellar Cortex −1.278e-03 (4.535e-04) −1.880 0.005 0.019*
Left Cerebellar White Matter −2.247e-04 (1.329e-04) −0.331 0.092 0.129
Right Cerebellar White Matter −1.490e-04 (1.350e-04) −0.219 0.271 0.316
Vermis I-V −3.756e-05 (6.450e-05) −0.055 0.561 0.561
Vermis VI-VII −5.293e-05 (2.934e-05) −0.078 0.073 0.129
Vermis VIII-X −6.592e-05(3.825E-05) −0.097 0.086 0.129
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A total of 211 participants (81 Ctrl, 131 BD) were included in the analysis, and age, sex, and scanner were used as covariates. P-values were FDR-corrected. Significant differences are indicated (q < 0.05, *).

Association of genetic loading for bipolar disorder with cerebellar volumes.

We subsequently explored whether genetic loading for bipolar disorder would be associated with cerebellar volume. First, we calculated polygenic propensity score (PGS) for bipolar disorder for all participants with available data (N = 103 bipolar disorder and N = 64 control). As expected, participants with bipolar disorder had significantly higher bipolar PGS than controls (t-test, t(165) = −4.856, p < 0.001; Supplemental Figure 2), suggesting that this summary measure of genetic loading has predictive value for bipolar disorder. Next, we tested whether bipolar PGS would predict any cerebellar volumes across both diagnosis groups, adjusting for age and sex (Table 3). Bipolar PGS did not predict any cerebellar volumes (all p > 0.05 and all q > 0.05). This suggests that overall genetic loading may not explain the observed differences in gray matter volumes between participants with bipolar disorder and controls.

Table 3.

Bipolar polygenic propensity score (PGS) was not associated with differences in cerebellar volumes.

Effect of bipolar PGS (N = 167)
Cerebellar Volume (proportion of ICV) Beta Coefficient Estimate (SE) p-value q-value
Left Cerebellar Cortex −3.761e-04 (2.372e-04) 0.115 0.223
Right Cerebellar Cortex −3.608e-04 (2.354e-04) 0.127 0.223
Left Cerebellar White Matter −9.550e-05 (6.962e-05) 0.172 0.241
Right Cerebellar White Matter −1.130e-04 (6.851e-05) 0.101 0.223
Vermis I-V −1.014e-05 (3.452e-05) 0.769 0.769
Vermis VI-VII 1.888e-05 (1.651e-05) 0.255 0.297
Vermis VIII-X −3.174e-05 (2.002e-05) 0.115 0.223
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A total of 167 participants (64 Ctrl, 103 BD) had available bipolar PGS and structural scans and were included in the analysis. Age, sex, and scanner were used as covariates. P-values were FDR-corrected.

Effect of adverse childhood experiences on cerebellar volumes in bipolar disorder.

To better understand what may contribute to the variation in cerebellar volumes within participants with bipolar disorder, we explored whether either parental history of psychiatric disorders or number of adverse childhood experiences (ACE score) was associated with cerebellar volumes (Table 4). As previously mentioned, ACE score is not independent from familial history of psychiatric illness. In addition, familial history of psychiatric illness also contains a genetic contribution. With these limitations in mind, parental psychiatric history did not predict any cerebellar volumes (N = 118; all p > 0.05 and q > 0.05), while increasing ACE score corresponded to larger proportionate volumes of cerebellar white matter (N = 97; left: p = 0.005, q = 0.033; right: p = 0.012, q = 0.042). However, since previous literature has reported that ACEs are associated with reduced ICV (Gehred et al., 2021), we checked for a correlation between ACE score and ICV, and found a negative correlation (Supplemental Figure 3, Pearson correlation = −0.275, t(95) = −2.787, p = 0.006). To determine whether the cerebellar proportionate volume effects were driven by changes in ICV, we added a covariate (Supplemental Table 2) and found that while both left and right cerebellar white matter proportionate volumes were still nominally larger with increasing ACEs, these effects did not survive FDR correction (left: p = 0.019, q = 0.138; right: p = 0.046, q = 0.161). This suggests that changes in ICV may contribute to the observed differences in proportionate cerebellar volumes.

Table 4.

Within participants with bipolar disorder, parental history of psychiatric illness was not associated with differences in cerebellar volumes; adverse childhood experience (ACE) score was associated with larger cerebellar white matter volumes bilaterally, but this may have been explained in part by differences in ICV (see Supplemental Table 2).

Variable of interest (BD only) Cerebellar Volume (prop. of ICV) Beta Coefficient Estimate (SE) p-value q-value
Parental Psychiatric History (N “Yes” = 92, N “No” = 26) Left Cerebellar Cortex 4.423e-04 (7.177e-04) 0.539 0.834
Right Cerebellar Cortex 3.880e-04 (7.131e-04) 0.587 0.834
Left Cerebellar White Matter 2.103e-04 (2.245e-04) 0.351 0.834
Right Cerebellar White Matter 1.137e-04 (2.206e-04) 0.607 0.834
Vermis I-V 1.398e-04 (1.100e-04) 0.207 0.834
Vermis VI-VII −1.772e-05 (4.831e-05) 0.714 0.834
Vermis VIII-X −3.773e-06 (6.403e-05) 0.953 0.953
ACE Score (N = 97) Left Cerebellar Cortex 2.497e-04 (1.197e-04) 0.040 0.070
Right Cerebellar Cortex 2.472e-04 (1.180e-04) 0.039 0.070
Left Cerebellar White Matter 1.092e-04 (3.773e-05) 0.005 0.033*
Right Cerebellar White Matter 9.740e-05 (3.804e-05) 0.012 0.042*
Vermis I-V 2.855e-05 (1.866e-05) 0.129 0.151
Vermis VI-VII 1.377e-05 (7.944e-06) 0.086 0.121
Vermis VIII-X 9.346e-06 (1.107e-05) 0.401 0.401
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These variables were tested in separate models with age, sex, and scanner as covariates. P-values were FDR-corrected. Significant results are indicated (q < 0.05, *).

Associations of duration of illness and symptom burden with cerebellar volumes.

Since previous work has suggested that an increased number of episodes or illness duration may be associated with changes in cerebellar volumes in bipolar disorder (DelBello et al., 1999; Lisy et al., 2011; Monkul et al., 2008; Moorhead et al., 2007; Yates, 1987), we tested whether duration of illness or self-reported time in a mood state (euthymic, depressed, or manic) would predict cerebellar volumes in participants with bipolar disorder (Supplemental Table 3). No associations between these measures and cerebellar volumes were identified (p > 0.05 and q > 0.05 for all cerebellar regions of interest).

Associations of medications with cerebellar volumes.

Finally, we assessed whether a given class of psychotropic medication was associated with cerebellar volumes within bipolar disorder (Supplemental Table 4). For this analysis, we compared participants with bipolar disorder not taking a given class of medication to participants with bipolar disorder who were taking the class of medication. We did not analyze stimulant medication because use was infrequent (N = 13). We expected to observe differences in cerebellar volumes in those taking antipsychotics or lithium as compared to the other classes of medications based on previous reports, primarily in other brain areas (Fusar-Poli et al., 2013; Hallahan et al., 2011; Jones et al., 2022; Kim et al., 2013; Lyoo et al., 2010; Sun et al., 2018). Interestingly, the largest effects were instead observed with sedatives, a category including benzodiazepines, sleep medications, barbiturates, and melatonin. Sedatives were associated with larger cerebellar volumes in left and right cerebellar white matter (left: p = 0.003, q = 0.010; right: p < 0.001, q = 0.003). Additionally, bipolar participants taking sedatives tended to have larger cerebellar gray matter, but neither hemisphere was significant after correction (left: p = 0.084, q = 0.147; right: p = 0.037, q = 0.087). Demographic information on participants with bipolar disorder taking and not taking sedatives is available in Supplemental Table 5. Importantly, no group differences in intracranial volume were observed. To understand how these groups compared to control participants not taking sedatives, we evaluated the three groups using ANCOVAs (Figure 2; Supplemental Table 6). In general, we found that control participants and bipolar participants taking sedatives had similar non-vermal cerebellar volumes, while the bipolar participants not taking sedatives had smaller non-vermal volumes.

Figure 2.

Figure 2.

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Bipolar participants taking sedative medication (BD (+), N = 56) had similar cerebellar white matter and gray matter volumes to control participants not on sedative medication (Ctrl (−), N = 75), while bipolar participants not taking sedatives generally had smaller volumes than the other groups (BD (−), N = 75). Graphs show partial residuals for diagnosis/medication group variable from models controlling for age, sex, and scanner. Group comparisons with significant differences (Tukey-adjusted p-values < 0.05) are marked with asterisks (* for p < 0.05, ** for p < 0.01).

Discussion

Here we report smaller cerebellar gray matter volumes bilaterally in individuals with bipolar disorder type I compared to controls. This difference did not extend to cerebellar white matter or vermal tissue and appeared diffuse throughout much of the cerebellar cortex. We found genetic loading for bipolar disorder did not explain the difference in gray matter, but that several variables may be associated with cerebellar white matter volumes within participants with bipolar disorder, including current use of sedative medication and adverse childhood experiences (ACEs). These findings add to the growing body of literature on cerebellar volumes in bipolar disorder and further highlight the need for nuanced study of the cerebellum as a potential player in bipolar disorder pathophysiology.

Our finding of reduced cerebellar hemisphere gray matter in bipolar disorder is consistent with several studies in the literature (de Zwarte et al., 2019; Lin et al., 2018; Moorhead et al., 2007; Sarıçiçek et al., 2015). It is important to note that our study sought to overcome potential limitations in the literature by including larger sample sizes and specifying a single bipolar disorder subtype. In literature covering cerebellar volumes in bipolar disorder, our study’s sample size is second only to the multi-site ENIGMA consortium analysis, which also found smaller cerebellar gray matter volumes in bipolar disorder but did not distinguish between bipolar disorder subtypes (de Zwarte et al., 2019). What might be causing the smaller cerebellar volumes remains unknown. One possibility is that the cerebellum may atrophy in bipolar disorder. A longitudinal design would be needed to directly assess atrophy, and our cross-sectional study was not designed to capture changes over time. However, we did not observe correlation between duration of illness or symptom burden with cerebellar volumes, which would have been consistent with atrophy during disease course. It is possible that cerebellar volume changes occurred prior to diagnosis. This could indicate that bipolar disorder is associated with altered cerebellar development, which has been suggested to be especially sensitive to environmental factors due to protracted time course (Ciesielski and Knight, 1994). Consistent with this possibility, young people at risk for bipolar disorder have different cognitive task activation patterns in the cerebellum (Johnsen et al., 2020) and are impaired on a cerebellar-dependent motor task (Giles et al., 2008). Furthermore, differences in white matter microstructure (Linke et al., 2020) and functional connectivity (Roberts et al., 2022) have been observed in brains of youth at risk for bipolar disorder; however, the cerebellum has been understudied in this area. Further work is needed to examine the intersection of cerebellar development and bipolar disorder.

Although neurodevelopmental differences might contribute to smaller cerebellar gray matter volumes in people with bipolar disorder type I versus controls, an increasing number of ACEs within the bipolar group did not correspond with any reductions in cerebellar volume. Rather, depending on covariates used, cerebellar white matter proportionate volumes appeared to be either larger or preserved. This occurred despite total ICV decreasing with ACE score. This may contrast with a previous study in a population birth cohort that found that cerebellar volumes decreased with increasing ACEs, which paralleled reductions in ICV (Gehred et al., 2021). However, that study did not examine bipolar disorder specifically. It is possible that the cerebellum is affected differently with ACEs in bipolar disorder than in the general population, potentially in a compensatory manner; further study in this area is required.

It is intriguing that bipolar PGS was not associated with changes in cerebellar gray matter volume while bipolar I diagnosis was, which suggests that the observed differences from controls were not reliant on genetic loading. However, there is precedence for psychiatric polygenic scores to not correlate with brain volume differences, or for correlations to be very weak. For example, Reus and colleagues did not find any associations between polygenic risk score for bipolar disorder and any brain volumes examined; there were also no associations for major depression and only one association for schizophrenia (Reus et al., 2017). Similarly, other studies of schizophrenia found no associations between the significant differences in regional volumes and polygenic risk scores (Alnæs et al., 2019) and that genome-wide schizophrenia polygenic risk scores still had no associations with brain volumes even with increasing the sample size to almost 15,000 (Grama et al., 2020). There are several possibilities for this type of discrepancy. One possibility is that the SNPs included in the PGS calculation do not capture the genetic differences responsible for reduced cerebellar gray matter. For example, if rare variants contribute to changes in cerebellar volume, these would not be included in the calculation of PGS, which could explain the lack of association. Another possibility is that the genetic underpinnings of reduced cerebellar gray matter are weaker predictors of bipolar disorder than other genes, and thus other genetic changes are predominantly determining the bipolar PGS and drown out the signal. A more refined, non-genome-wide PGS might be better able to capture specific phenotypes, such as cerebellar volume changes; this has previously been suggested to be a better approach for explaining brain structural changes in schizophrenia (Grama et al., 2020). There may also be environmental contributions resulting in structural changes. As environmental influences can change gene expression via epigenetic mechanisms, additional studies of alterations in gene expression in bipolar disorder would be beneficial. Finally, we cannot rule out complex interactions between certain genetic risk factors and other variables such as age and sex, or epistatic interactions between genetic variants, which could mask true associations.

An important consideration in structural differences related to bipolar disorder involves medication exposure. In this cross-sectional study, we found sedative use in participants with bipolar disorder was significantly associated with larger cerebellar white matter volumes. No other class of medications showed a significant relationship with cerebellar volumes that survived FDR correction. Thus, these findings suggest that medications were not likely the cause of the reduced cerebellar gray matter when comparing participants with bipolar disorder to controls. However, we cannot rule out this possibility given the cross-sectional nature of the study. Prior studies have observed structural brain changes associated with antipsychotic and lithium treatment (Fusar-Poli et al., 2013; Hallahan et al., 2011; Jones et al., 2022; Kim et al., 2013; Lyoo et al., 2010; Sun et al., 2018). However, only one prior study (Huhtaniska et al., 2017) has been published reporting effects of sedatives on brain structure. This study found that cumulative benzodiazepine dose was negatively associated with caudate volume after controlling for cumulative antipsychotic dose and symptom severity. In our study, we observed an increase in cerebellar volumes with participants being on a sedative. Furthermore, our study did not evaluate the effect of medications on the caudate since we were focused on factors that may influence findings in the cerebellum. Taken together, one possible explanation is that the cerebellum is compensating for caudate atrophy. An alternative possibility could be that our medication findings are a byproduct of confounding variables, such as differences in age.

However, participants with bipolar disorder taking sedatives were older than those not taking sedatives, which would not explain why their cerebellar white matter volumes were larger. Finally, there may be complex interactions between variables, such as those arising from polypharmacy, which this study did not address. Regardless of the underlying reasons, these findings raise the possibility that medication effects could diminish or mask true volume differences in bipolar disorder. Thus, cerebellar volume differences in bipolar disorder could be even greater without the confounds of medications. Future studies are needed to follow up on this possibility, and to investigate whether sedatives also associate with altered cerebellar volumes in other psychiatric disorders.

The described work does come with some limitations. As mentioned, this study was cross-sectional, which limits our ability to understand how and when cerebellar differences emerge in bipolar disorder. We allowed psychiatric comorbidities other than recent substance or alcohol abuse in the bipolar group, which is consistent with the complex presentation typical of many individuals with bipolar disorder (Merikangas et al., 2007) but may have introduced variation. This study design also limited analysis of medication effects, as we only examined current medications and not cumulative dosing. Furthermore, the presence of frequent polypharmacy was not addressed, and represents an important caveat. Finally, our sample was primarily White (88.7%), and thus our results may not generalize to all populations.

Overall, this paper supports the finding of smaller cerebellar cortices in bipolar disorder type I. Furthermore, it suggests that external sources such as medication exposure and childhood experiences may be important determinants of cerebellar volumes. Better understanding of cerebellar differences in bipolar disorder may help to advance understanding of bipolar disorder etiology, which may help lead to novel preventative measures or therapeutic options.

Supplementary Material

1

Highlights.

  • Cerebellar cortex volume is smaller in bipolar disorder type I

  • Genetic propensity for bipolar disorder did not explain smaller cerebellar volumes

  • Childhood experiences and sedatives were associated with altered cerebellar volumes

Funding and Acknowledgements

This work was supported by NIMH grant R01MH111578, funding from the Roy J. Carver Charitable Trust, NCATS grant UL1TR002537, NIH Instrumentation grants S10OD025025 and S10RR028821, and an Iowa Neuroscience Institute Research Program of Excellence Award. J.A.W. was also supported by a Merit Award from the U.S. Department of Veterans Affairs, and G.I.S.H. was supported by NIMH grant T32MH019113. None of the funding sources influenced any aspect of study design, data collection, analysis, or writing of the manuscript.

Processing and analysis were supported in part through computational resources provided by The University of Iowa.

Footnotes

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All authors declare no conflicts of interest.

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