Relationships between serotonin 1A receptor DNA methylation, self-reported history of childhood abuse and gray matter volume in major depression.

BACKGROUND: Early life adversity is a risk factor for psychopathology and is associated with epigenetic alterations in the 5-HT1A receptor gene promoter. The 5-HT1A receptor mediates neurotrophic effects, which could affect brain structure and function. We examined relationships between self-reported early childhood abuse, 5-HT1A receptor promoter DNA methylation, and gray matter volume (GMV) in Major Depressive Disorder (MDD). METHODS: Peripheral DNA methylation of 5-HT1A receptor promoter CpG sites -681 and -1007 was assayed in 50 individuals with MDD, including 18 with a history of childhood abuse. T1-weighted structural magnetic resonance imaging (MRI) was performed. Voxel-based morphometry (VBM) was quantified in amygdala, hippocampus, insula, occipital lobe, orbitofrontal cortex, temporal lobe, parietal lobe, and at the voxel level. RESULTS: No relationship was observed between DNA methylation and history of childhood abuse. We observed regional heterogeneity comparing -681 CpG site methylation and GMV (p = 0.014), with a positive relationship to GMV in orbitofrontal cortex (p = 0.035). Childhood abuse history was associated with higher GMV considering all ROIs simultaneously (p < 0.01). In whole-brain analyses, childhood abuse history was positively correlated with GMV in multiple clusters, including insula and orbitofrontal cortex (pFWE = 0.005), and negatively in intracalcarine cortex (pFWE = 0.001). LIMITATIONS: Small sample size, childhood trauma assessment instrument used, and assay of peripheral, rather than CNS, methylation. CONCLUSIONS: These cross-sectional findings support hypotheses of 5-HT1A receptor-related neurotrophic effects, and of increased regional GMV as a potential regulatory mechanism in the setting of childhood abuse. Orbitofrontal cortex was uniquely associated with both childhood abuse history and 5-HT1A receptor methylation.

Unsupervised Bayesian classification for models with scalar and functional covariates.

We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of [11C]WAY100635 Overcomes Outcome Measure Discrepancies.

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-11C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [11C]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [11C]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches therefore demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

Functional support vector machine.

Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the functional predictors is complex. On the other hand, support vector machines (SVMs) are among the most robust prediction models but do not take account of the high correlations between repeated measurements and cannot be used for irregular data. In this work, we propose a novel method to integrate functional principal component analysis with SVM techniques for classification and regression to account for the continuous nature of functional data and the nonlinear relationship between the scalar response variable and the functional predictors. We demonstrate the performance of our method through extensive simulation experiments and two real data applications: the classification of alcoholics using electroencephalography signals and the prediction of glucobrassicin concentration using near-infrared reflectance spectroscopy. Our methods especially have more advantages when the measurement errors in functional predictors are relatively large.

Nonlinear Mixed-Effects Modeling Approach for Simplified Reference Tissue Model.

OBJECTIVE: The conventional approach to the analysis of dynamic PET data can be described as a two-stage approach. In Stage 1, each individual's kinetic parameter estimates are obtained by modeling their PET data. Then in Stage 2, those parameter estimates are treated as though they are the observed data and compared across subjects and groups using standard statistical analyses. In this context, we explore the application of nonlinear mixed-effects (NLME) model under the assumptions of simplified reference tissue model. METHODS: In the NLME framework, all subject's PET data are modeled simultaneously and the estimation of kinetic parameters and statistical inference across subjects are performed jointly. RESULTS: In simulated [ 11C]WAY100635 data, this NLME approach shows improved power (6-27% increase) for detecting group differences and greater consistency of population (1.13-1.44 times greater) and individual-level parameter estimation compared to the two-stage approach applying simplified reference tissue model for pharmacokinetic modeling of PET data. We applied our NLME approach to clinical PET data and observed shrinkage of individual-level parameters that is inherent in this modeling structure. CONCLUSION: The proposed approach is more powerful and accurate than the two-stage approach under the assumptions of simplified reference tissue model in PET data. SIGNIFICANCE: The stability of the NLME approach not only improves the efficiency of collected data, but also comes with no additional financial cost and negligible computation cost.

Dynamic Human Brain Imaging with a Portable PET Camera: Comparison to a Standard Scanner.

Portable, cost-effective PET cameras can radically expand the applicability of PET. We present here a within-participant comparison of fully quantified [18F]FDG dynamic scans in healthy volunteers using the standard Biograph mCT scanner and portable CerePET scanner. Methods: Each of 20 healthy volunteers underwent dynamic [18F]FDG imaging with both scanners (1-154 d apart) and concurrent arterial blood sampling. Tracer SUV, net influx rate (Ki), and the corresponding cerebral metabolic rate of glucose (CMRglu) were quantified at regional and voxel levels. Results: At the regional level, CerePET outcome measure estimates within participants robustly correlated with Biograph mCT estimates in the neocortex, wherein the average Pearson correlation coefficients across participants ± SD were 0.83 ± 0.07 (SUV) and 0.85 ± 0.08 (Ki and CMRglu). There was also strong agreement between CerePET and Biograph mCT estimates, wherein the average regression slopes across participants were 0.84 ± 0.17 (SUV), 0.83 ± 0.17 (Ki), and 0.85 ± 0.18 (CMRglu). There was similar bias across participants but higher correlation and less variability in subcortical regions than in cortical regions. Pearson correlation coefficients for subcortical regions equaled 0.97 ± 0.02 (SUV) and 0.97 ± 0.03 (Ki and CMRglu), and average regression slopes equaled 0.79 ± 0.14 (SUV), 0.83 ± 0.11 (Ki), and 0.86 ± 0.11 (CMRglu). In voxelwise assessment, CerePET and Biograph mCT estimates across outcome measures were significantly different only in a cluster of left frontal white matter. Conclusion: Our results indicate robust correlation and agreement between semi- and fully quantitative brain glucose metabolism measurements from portable CerePET and standard Biograph mCT scanners. The results obtained with a portable PET scanner in this comparison in humans require follow-up but lend confidence to the feasibility of more flexible and portable brain imaging with PET.

Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging.

The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterize the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilize function-on-scalar regression modeling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions is made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a "block bootstrapping" approach from time series analysis to the functional data setting.

Parametric and non-parametric Poisson regression for modelling of the arterial input function in positron emission tomography.

Full quantification of Positron Emission Tomography (PET) requires an arterial input function (AIF) for measurement of certain targets, or using particular radiotracers, or for the quantification of specific outcome measures. The AIF represents the measurement of radiotracer concentrations in the arterial blood plasma over the course of the PET examination. Measurement of the AIF is prone to error as it is a composite measure created from the combination of multiple measurements of different samples with different equipment, each of which can be sources of measurement error. Moreover, its measurement requires a high degree of temporal granularity for early time points, which necessitates a compromise between quality and quantity of recorded samples. For these reasons, it is often desirable to fit models to this data in order to improve its quality before using it for quantification of radiotracer binding in the tissue. The raw observations of radioactivity in arterial blood and plasma samples are derived from radioactive decay, which is measured as a number of recorded counts. Count data have several specific properties, including the fact that they cannot be negative as well as a particular mean-variance relationship. Poisson regression is the most principled modelling strategy for working with count data, as it both incorporates and exploits these properties. However, no previous studies to our knowledge have taken this approach, despite the advantages of greater efficiency and accuracy which result from using the appropriate distributional assumptions. Here, we implement a Poisson regression modelling approach for the AIF as proof-of-concept of its application. We applied both parametric and non-parametric models for the input function curve. We show that a negative binomial distribution is a more appropriate error distribution for handling overdispersion. Furthermore, we extend this approach to a hierarchical non-parametric model which is shown to be highly resilient to missing data. We thus demonstrate that Poisson regression is both feasible and effective when applied to AIF data, and propose that this is a promising strategy for modelling blood count data for PET in future.

Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome.

This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.

Kinetic models for estimating occupancy from single-scan PET displacement studies.

The traditional design of PET target engagement studies is based on a baseline scan and one or more scans after drug administration. We here evaluate an alternative design in which the drug is administered during an on-going scan (i.e., a displacement study). This approach results both in lower radiation exposure and lower costs. Existing kinetic models assume steady state. This condition is not present during a drug displacement and consequently, our aim here was to develop kinetic models for analysing PET displacement data. We modified existing compartment models to accommodate a time-variant increase in occupancy following the pharmacological in-scan intervention. Since this implies the use of differential equations that cannot be solved analytically, we developed instead one approximate and one numerical solution. Through simulations, we show that if the occupancy is relatively high, it can be estimated without bias and with good accuracy. The models were applied to PET data from six pigs where [11C]UCB-J was displaced by intravenous brivaracetam. The dose-occupancy relationship estimated from these scans showed good agreement with occupancies calculated with Lassen plot applied to baseline-block scans of two pigs. In summary, the proposed models provide a framework to determine target occupancy from a single displacement scan.

Multivariate analysis of PET pharmacokinetic parameters improves inferential efficiency.

PURPOSE: In positron emission tomography quantification, multiple pharmacokinetic parameters are typically estimated from each time activity curve. Conventionally all but the parameter of interest are discarded before performing subsequent statistical analysis. However, we assert that these discarded parameters also contain relevant information which can be exploited to improve the precision and power of statistical analyses on the parameter of interest. Properly taking this into account can thereby draw more informative conclusions without collecting more data. METHODS: By applying a hierarchical multifactor multivariate Bayesian approach, all estimated parameters from all regions can be analysed at once. We refer to this method as Parameters undergoing Multivariate Bayesian Analysis (PuMBA). We simulated patient-control studies with different radioligands, varying sample sizes and measurement error to explore its performance, comparing the precision, statistical power, false positive rate and bias of estimated group differences relative to univariate analysis methods. RESULTS: We show that PuMBA improves the statistical power for all examined applications relative to univariate methods without increasing the false positive rate. PuMBA improves the precision of effect size estimation, and reduces the variation of these estimates between simulated samples. Furthermore, we show that PuMBA yields performance improvements even in the presence of substantial measurement error. Remarkably, owing to its ability to leverage information shared between pharmacokinetic parameters, PuMBA even shows greater power than conventional univariate analysis of the true binding values from which the parameters were simulated. Across all applications, PuMBA exhibited a small degree of bias in the estimated outcomes; however, this was small relative to the variation in estimated outcomes between simulated datasets. CONCLUSION: PuMBA improves the precision and power of statistical analysis of PET data without requiring the collection of additional measurements. This makes it possible to study new research questions in both new and previously collected data. PuMBA therefore holds great promise for the field of PET imaging.

The Temporal Dynamics of Emotion Regulation in Subjects With Major Depression and Healthy Control Subjects.

BACKGROUND: Emotion regulation (ER) processes help support well-being, but ineffective ER is implicated in several psychiatric disorders. Engaging ER flexibly by going online and offline as needs and capacities shift may be more effective than engaging ER rigidly across time. Here, we sought to observe the neural temporal dynamics of an ER process, reappraisal, during regulation of responses to negative memories in healthy control subjects (n = 33) and subjects with major depressive disorder (n = 36). METHODS: To track the temporal dynamics of reappraisal neural systems, we used a functional magnetic resonance imaging neural decoding approach. In task 1, subjects explicitly engaged reappraisal on instruction in response to aversive images, and we used this task to develop the decoder for detecting reappraisal. In task 2, subjects experienced negative autobiographical memories from a distant (third person, ER condition) or immersed (first person, control condition) perspective. RESULTS: The neural decoder, trained to detect reappraisal in task 1, predicted greater reappraisal occurring during the task 2 distance versus immerse trials and was engaged more intensely during memories that were rated as being more negative. Across time, decoder output manifested a temporal dynamic of early engagement followed by disengagement. These results were replicated in an independent subject dataset (n = 59). Relative to healthy control subjects, subjects with major depressive disorder had a comparable initial increase in decoder engagement at the beginning of the trial but an attenuated decrease at the end. CONCLUSIONS: Subjects with major depressive disorder evidenced a more rigid neural dynamic of reappraisal compared with healthy control subjects. Rigid ER may indicate diminished ability to flexibly and effectively regulate emotion.

Functional additive models for optimizing individualized treatment rules.

A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.

Mature parvalbumin interneuron function in prefrontal cortex requires activity during a postnatal sensitive period.

In their seminal findings, Hubel and Wiesel identified sensitive periods in which experience can exert lasting effects on adult visual cortical functioning and behavior via transient changes in neuronal activity during development. Whether comparable sensitive periods exist for non-sensory cortices, such as the prefrontal cortex, in which alterations in activity determine adult circuit function and behavior is still an active area of research. Here, using mice we demonstrate that inhibition of prefrontal parvalbumin (PV)-expressing interneurons during the juvenile and adolescent period, results in persistent impairments in adult prefrontal circuit connectivity, in vivo network function, and behavioral flexibility that can be reversed by targeted activation of PV interneurons in adulthood. In contrast, reversible suppression of PV interneuron activity in adulthood produces no lasting effects. These findings identify an activity-dependent sensitive period for prefrontal circuit maturation and highlight how abnormal PV interneuron activity during development alters adult prefrontal circuit function and cognitive behavior.

Mu Opioid Receptor Dynamics in Healthy Volunteers with a History of Childhood Maltreatment.

Evidence suggests that adults with a history of childhood maltreatment, the experience of emotional or physical neglect and/or abuse within the family during childhood, have blunted reward and stress processing, and higher risk of depression. The mu opioid receptor rich nucleus accumbens and amygdala are critical to reward and stress processing respectively. We hypothesized that nucleus accumbens and amygdala mu opioid receptor densities and activity (change in receptor binding due to endogenous opioid release or receptor conformation change) were negatively associated with childhood maltreatment in healthy young adults. Maltreatment was assessed with the Childhood Trauma Questionnaire (CTQ). Healthy participants, n = 75 (52% female) completed [11C]carfentanil positron emission tomography imaging labeling mu opioid receptors. The relationship between CTQ score and binding potential (BPND, proportional to density of unoccupied receptors) was evaluated with a linear mixed effects model. No significant relationship was found between CTQ score and BPND (f = 3.28; df = 1, 73; p = 0.074) or change in BPND (activity) (t = 1.48; df = 198.3; p = 0.14). This is the first investigation of mu opioid receptors in those with childhood maltreatment. We did not identify a significant relationship between mu opioid receptor dynamics and severity of maltreatment in those without psychopathology. Because this cohort has a low CTQ score average, this may indicate that those with low severity of maltreatment may not have associated changes in mu opioid receptor dynamics. Future directions include evaluating a cohort with increased severity of childhood maltreatment.

Designing drug occupancy studies with PET neuroimaging: Sample size, occupancy ranges and analytical methods.

Molecular neuroimaging is today considered essential for evaluation of novel CNS drugs; it is used to quantify blood-brain barrier permeability, verify interaction with key target and determine the drug dose resulting in 50% occupancy, IC50. In spite of this, there has been limited data available to inform on how to optimize study designs. Through simulations, we here evaluate how IC50 estimation is affected by the (i) range of drug doses administered, (ii) number of subjects included, and (iii) level of noise in the plasma drug concentration measurements. Receptor occupancy is determined from PET distribution volumes using two different methods: the Lassen plot and Likelihood estimation of occupancy (LEO). We also introduce and evaluate a new likelihood-based estimator for direct estimation of IC50 from PET distribution volumes. For estimation of IC50, we find very limited added benefit in scanning individuals who are given drug doses corresponding to less than 40% receptor occupancy. In the range of typical PET sample sizes (5-20 subjects) each extra individual clearly reduces the error of the IC50 estimate. In all simulations, likelihood-based methods gave more precise IC50 estimates than the Lassen plot; four times the number of subjects were required for the Lassen plot to reach the same IC50 precision as LEO.

A single-index model with a surface-link for optimizing individualized dose rules.

This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression. The primary motivation is to estimate an optimal individualized dose rule and individualized treatment effects. To model possibly nonlinear interaction effects between patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear projection of the covariates. The method is illustrated using two applications as well as simulation experiments. A unique contribution of this work is in the parsimonious (single-index) parametrization specifically defined for the interaction effect term.

Rejection sensitivity and mu opioid receptor dynamics associated with mood alterations in response to social feedback.

Rejection sensitivity (RS) is the heightened expectation or perception of social rejection and is a feature of many psychiatric disorders. As endogenous opioid pathways have been implicated in response to social rejection and reward, we hypothesize that RS will be negatively associated with mu opioid receptor (MOR) baseline binding and activity during rejection and acceptance stimuli. In exploratory analyses, we assessed the relationships between MOR activity and changes in mood and self-esteem before and after stimuli. Healthy participants, N = 75 (52% female), completed rejection and acceptance tasks during [11C]carfentanil positron emission tomography (PET) scans. MOR activity in the amygdala, midline thalamus, anterior insula, and nucleus accumbens (NAc) was evaluated. RS was not related to MOR baseline binding potential or activity during acceptance or rejection tasks in any region. Increased MOR activity in the NAc was associated with increase in ratings of self-esteem and positive mood during the period between acceptance task administration and approximately 5 min after the task completion. Our results suggest that endogenous opioid response to social rejection is independent of RS in healthy individuals. MOR activity in the NAc was associated with increase self-esteem and positive mood after experiencing social feedback, warranting further investigation.

Simultaneous multifactor Bayesian analysis (SiMBA) of PET time activity curve data.

Positron emission tomography (PET) is an in vivo imaging method essential for studying the neurochemical pathophysiology of psychiatric and neurological disease. However, its high cost and exposure of participants to radiation make it unfeasible to employ large sample sizes. The major shortcoming of PET imaging is therefore its lack of power for studying clinically-relevant research questions. Here, we introduce a new method for performing PET quantification and analysis called SiMBA, which helps to alleviate these issues by improving the efficiency of PET analysis by exploiting similarities between both individuals and regions within individuals. In simulated [11C]WAY100635 data, SiMBA greatly improves both statistical power and the consistency of effect size estimation without affecting the false positive rate. This approach makes use of hierarchical, multifactor, multivariate Bayesian modelling to effectively borrow strength across the whole dataset to improve stability and robustness to measurement error. In so doing, parameter identifiability and estimation are improved, without sacrificing model interpretability. This comes at the cost of increased computational overhead, however this is practically negligible relative to the time taken to collect PET data. This method has the potential to make it possible to test clinically-relevant hypotheses which could never be studied before given the practical constraints. Furthermore, because this method does not require any additional information over and above that required for traditional analysis, it makes it possible to re-examine data which has already previously been collected at great expense. In the absence of dramatic advancements in PET image data quality, radiotracer development, or data sharing, PET imaging has been fundamentally limited in the scope of research hypotheses which could be studied. This method, especially combined with the recent steps taken by the PET imaging community to embrace data sharing, will make it possible to greatly improve the research possibilities and clinical relevance of PET neuroimaging.

Resting State MRI Amplitude of Low Frequency Fluctuations Associated With Suicidal Ideation in Bipolar Depression.

Objective: Suicidal ideation (SI) is a risk factor for completed suicide. Our previous resting state functional magnetic resonance imaging (fMRI) study found that higher amplitude of low frequency fluctuation (ALFF) in right hippocampus and thalamus was associated with SI in major depressive disorder (MDD). The present study aimed to evaluate that association in participants with bipolar disorder (BD).Methods: Thirty depressed, adult participants with a DSM-IV diagnosis of BD had resting state fMRI scans. Region-of-interest (ROI) analyses used ALFF values within areas that were previously associated with SI in MDD. Spearman rank correlation and ordinal regression analyses were performed to assess associations between ALFF values and the SI item of the Montgomery-Asberg Depression Rating Scale. Exploratory whole-brain analyses identified regions where ALFF was associated with SI.Results: Within the right hippocampus region, SI was positively associated with ALFF (Spearman R = 0.490, P = .0060). Ordinal regression analysis indicated that for every 0.1-unit increase in ALFF in that region, the odds of having higher SI were increased by 35% (odds ratio = 1.35; 95% confidence interval, 1.08-1.73; P = .012). Within the previously identified thalamus cluster, SI was associated with ALFF only at a trend level (Spearman R = 0.310, P = .069). Whole-brain analyses identified 3 clusters of positive association between SI and ALFF, 1 of which was located in the right hippocampus.Conclusions: This study found that our previous finding of positive association between SI and ALFF in the right hippocampus extended to bipolar depression. Future studies should examine the clinical utility of this association, and the role of the hippocampus in SI.Trial Registration: Data used for this secondary analysis came from studies with ClinicalTrials.gov identifiers NCT02239094 (January 2015 through September 2016) and NCT02473250 (January 2015 through December 2019).