Quantification of Positron Emission Tomography Data Using Simultaneous Estimation of the Input Function: Validation with Venous Blood and Replication of Clinical Studies

Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain parametric imaging

Dynamic PET allows quantification of physiological parameters through tracer kinetic modeling. For dynamic imaging of brain or head and neck cancer on conventional PET scanners with a short axial field of view, the image-derived input function (ID-IF) from intracranial blood vessels such as the carotid artery (CA) suffers from severe partial volume effects. Alternatively, optimization-derived input function (OD-IF) by the simultaneous estimation (SIME) method does not rely on an ID-IF but derives the input function directly from the data. However, the optimization problem is often highly ill-posed. We proposed a new method that combines the ideas of OD-IF and ID-IF together through a kernel framework. While evaluation of such a method is challenging in human subjects, we used the uEXPLORER total-body PET system that covers major blood pools to provide a reference for validation.

METHODS

The conventional SIME approach estimates an input function using a joint estimation together with kinetic parameters by fitting time activity curves from multiple regions of interests (ROIs). The input function is commonly parameterized with a highly nonlinear model which is difficult to estimate. The proposed kernel SIME method exploits the CA ID-IF as a priori information via a kernel representation to stabilize the SIME approach. The unknown parameters are linear and thus easier to estimate. The proposed method was evaluated using 18F-fluorodeoxyglucose studies with both computer simulations and 20 human-subject scans acquired on the uEXPLORER scanner. The effect of the number of ROIs on kernel SIME was also explored.

RESULTS

The estimated OD-IF by kernel SIME showed a good match with the reference input function and provided more accurate estimation of kinetic parameters for both simulation and human-subject data. The kernel SIME led to the highest correlation coefficient (R = 0.97) and the lowest mean absolute error (MAE = 10.5 %) compared to using the CA ID-IF (R = 0.86, MAE = 108.2 %) and conventional SIME (R = 0.57, MAE = 78.7 %) in the human-subject evaluation. Adding more ROIs improved the overall performance of the kernel SIME method.

CONCLUSION

The proposed kernel SIME method shows promise to provide an accurate estimation of the blood input function and kinetic parameters for brain PET parametric imaging.

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep-learning-based prediction model (DLIF) that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET

BACKGROUND

Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data.

METHODS

Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach.

RESULTS

Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns.

CONCLUSIONS

A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.

In vivo serotonin 1A receptor hippocampal binding potential in depression and reported childhood adversity

BACKGROUND

Reported childhood adversity (CA) is associated with development of depression in adulthood and predicts a more severe course of illness. Although elevated serotonin 1A receptor (5-HT_1AR) binding potential, especially in the raphe nuclei, has been shown to be a trait associated with major depression, we did not replicate this finding in an independent sample using the partial agonist positron emission tomography tracer [¹¹C]CUMI-101. Evidence suggests that CA can induce long-lasting changes in expression of 5-HT_1AR, and thus, a history of CA may explain the disparate findings.

METHODS

Following up on our initial report, 28 unmedicated participants in a current depressive episode (bipolar n = 16, unipolar n = 12) and 19 non-depressed healthy volunteers (HVs) underwent [¹¹C]CUMI-101 imaging to quantify 5-HT_1AR binding potential. Participants in a depressive episode were stratified into mild/moderate and severe CA groups via the Childhood Trauma Questionnaire. We hypothesized higher hippocampal and raphe nuclei 5-HT_1AR with severe CA compared with mild/moderate CA and HVs.

RESULTS

There was a group-by-region effect (p = 0.011) when considering HV, depressive episode mild/moderate CA, and depressive episode severe CA groups, driven by significantly higher hippocampal 5-HT_1AR binding potential in participants in a depressive episode with severe CA relative to HVs (p = 0.019). Contrary to our hypothesis, no significant binding potential differences were detected in the raphe nuclei (p-values > 0.05).

CONCLUSIONS

With replication in larger samples, elevated hippocampal 5-HT_1AR binding potential may serve as a promising biomarker through which to investigate the neurobiological link between CA and depression.

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (V_T, DVR, or BP_ND for reversible tracers; K_i or CMR_glu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R² = 0.59-1.00, R² = 0.71-1.00, R² = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R² = 0.71-1.00 and R² = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Development of a minimally invasive simultaneous estimation method for quantifying translocator protein binding with [18F]FEPPA positron emission tomography

BACKGROUND

The purpose of this study was to assess the feasibility of using a minimally invasive simultaneous estimation method (SIME) to quantify the binding of the 18-kDa translocator protein tracer [¹⁸F]FEPPA. Arterial sampling was avoided by extracting an image-derived input function (IDIF) that was metabolite-corrected using venous blood samples. The possibility of reducing scan duration to 90 min from the recommended 2-3 h was investigated by assuming a uniform non-displaceable distribution volume (V_ND) to simplify the SIME fitting.

RESULTS

SIME was applied to retrospective data from healthy volunteers and was comprised of both high-affinity binders (HABs) and mixed-affinity binders (MABs). Estimates of global V_ND and regional total distribution volume (V_T) from SIME were not significantly different from values obtained using a two-tissue compartment model (2CTM). Regional V_T estimates were greater for HABs compared to MABs for both the 2TCM and SIME, while the SIME estimates had lower inter-subject variability (41 ± 17% reduction). Binding potential (BP_ND) values calculated from regional V_T and brain-wide V_ND estimates were also greater for HABs, and reducing the scan time from 120 to 90 min had no significant effect on BP_ND. The feasibility of using venous metabolite correction was evaluated in a large animal model involving a simultaneous collection of arterial and venous samples. Strong linear correlations were found between venous and arterial measurements of the blood-to-plasma ratio and the remaining [¹⁸F]FEPPA fraction. Lastly, estimates of BP_ND and the specific distribution volume (i.e., V_S = V_T - V_ND) from a separate group of healthy volunteers (90 min scan time, venous-scaled IDIFs) agreed with estimates from the retrospective data for both genotypes.

CONCLUSIONS

The results of this study demonstrate that accurate estimates of regional V_T, BP_ND and V_S can be obtained by applying SIME to [¹⁸F]FEPPA data. Furthermore, the application of SIME enabled the scan time to be reduced to 90 min, and the approach worked well with IDIFs that were scaled and metabolite-corrected using venous blood samples.

Positron Emission Tomography (PET) Imaging Tracers for Serotonin Receptors

Serotonin (5-hydroxytryptamine, 5-HT) and 5-HT receptors (5-HTRs) have crucial roles in various neuropsychiatric disorders and neurodegenerative diseases, making them attractive diagnostic and therapeutic targets. Positron emission tomography (PET) is a noninvasive nuclear molecular imaging technique and is an essential tool in clinical diagnosis and drug discovery. In this context, numerous PET ligands have been developed for "visualizing" 5-HTRs in the brain and translated into human use to study disease mechanisms and/or support drug development. Herein, we present a comprehensive repertoire of 5-HTR PET ligands by focusing on their chemotypes and performance in PET imaging studies. Furthermore, this Perspective summarizes recent 5-HTR-focused drug discovery, including biased agonists and allosteric modulators, which would stimulate the development of more potent and subtype-selective 5-HTR PET ligands and thus further our understanding of 5-HTR biology.

Measuring brain glucose metabolism in order to predict response to antidepressant or placebo: A randomized clinical trial

There is critical need for a clinically useful tool to predict antidepressant treatment outcome in major depressive disorder (MDD) to reduce suffering and mortality. This analysis sought to build upon previously reported antidepressant treatment efficacy prediction from 2-[¹⁸F]-fluorodeoxyglucose - Positron Emission Tomography (FDG-PET) using metabolic rate of glucose uptake (MRGlu) from dynamic FDG-PET imaging with the goal of translation to clinical utility. This investigation is a randomized, double-blind placebo-controlled trial. All participants were diagnosed with MDD and received an FDG-PET scan before randomization and after treatment. Hamilton Depression Rating Scale (HDRS-17) was completed in participants diagnosed with MDD before and after 8 weeks of escitalopram, or placebo. MRGlu (mg/(min*100 ml)) was estimated within the raphe nuclei, right insula, and left ventral Prefrontal Cortex in 63 individuals. Linear regression was used to examine the association between pretreatment MRGlu and percent decrease in HDRS-17. Additionally, the association between percent decrease in HDRS-17 and percent change in MRGlu between pretreatment scan and post-treatment scan was examined. Covariates were treatment type (SSRI/placebo), handedness, sex, and age. Depression severity decrease (n = 63) was not significantly associated with pretreatment MRGlu in the raphe nuclei (β = -2.61e^-03 [-0.26, 0.25], p = 0.98), right insula (β = 0.05 [-0.23, 0.32], p = 0.72), or ventral prefrontal cortex (β = 0.06 [-0.23, 0.34], p = 0.68) where β is the standardized estimated coefficient, with a 95% confidence interval, or in whole brain voxelwise analysis (family-wise error correction, alpha = 0.05). MRGlu percent change was not significantly associated with depression severity decrease (n = 58) before multiple comparison correction in the RN (β = 0.20 [-0.07, 0.47], p = 0.15), right insula (β = 0.24 [-0.03, 0.51], p = 0.08), or vPFC (β = 0.22 [-0.06, 0.50], p = 0.12). We propose that FDG-PET imaging does not indicate a clinically relevant biomarker of escitalopram or placebo treatment response in heterogeneous major depressive disorder cohorts. Future directions include focusing on potential biologically-based subtypes of major depressive disorder by implementing biomarker stratified designs.

Serotonin 1A Receptor Binding of [11C]CUMI-101 in Bipolar Depression Quantified Using Positron Emission Tomography: Relationship to Psychopathology and Antidepressant Response

BACKGROUND

The pathophysiology of bipolar disorder (BD) remains largely unknown despite it causing significant disability and suicide risk. Serotonin signaling may play a role in the pathophysiology, but direct evidence for this is lacking. Treatment of the depressed phase of the disorder is limited. Previous studies have indicated that positron emission tomography (PET) imaging of the serotonin 1A receptor (5HT1AR) may predict antidepressant response.

METHODS

A total of 20 participants with BD in a current major depressive episode and 16 healthy volunteers had PET imaging with [11C]CUMI-101, employing a metabolite-corrected input function for quantification of binding potential to the 5HT1AR. Bipolar participants then received an open-labeled, 6-week clinical trial with a selective serotonin reuptake inhibitor (SSRI) in addition to their mood stabilizer. Clinical ratings were obtained at baseline and during SSRI treatment.

RESULTS

Pretreatment binding potential (BPF) of [11C]CUMI-101 was associated with a number of pretreatment clinical variables within BD participants. Within the raphe nucleus, it was inversely associated with the baseline Montgomery Åsberg Rating Scale (P = .026), the Beck Depression Inventory score (P = .0023), and the Buss Durkee Hostility Index (P = .0058), a measure of lifetime aggression. A secondary analysis found [11C]CUMI-101 BPF was higher in bipolar participants compared with healthy volunteers (P = .00275). [11C]CUMI-101 BPF did not differ between SSRI responders and non-responders (P = .907) to treatment and did not predict antidepressant response (P = .580). Voxel-wise analyses confirmed the results obtained in regions of interest analyses.

CONCLUSIONS

A disturbance of serotonin system function is associated with both the diagnosis of BD and its severity of depression. Pretreatment 5HT1AR binding did not predict SSRI antidepressant outcome.The study was listed on clinicaltrials.gov with identifier NCT02473250.

Brain 5-HT1A Receptor PET Binding, Cortisol Responses to Stress, and the Familial Transmission of Suicidal Behavior

BACKGROUND

The serotonin 1A (5-HT1A) receptor has been implicated in depression and suicidal behavior. Lower resting cortisol levels are associated with higher 5-HT1A receptor binding, and both differentiate suicide attempters with depression. However, it is not clear whether 5-HT1A receptor binding and cortisol responses to stress are related to familial risk and resilience for suicidal behavior.

METHODS

[11C]CUMI-101 positron emission tomography imaging to quantify regional brain 5-HT1A receptor binding was conducted in individuals considered to be at high risk for mood disorder or suicidal behavior on the basis of having a first- or second-degree relative(s) with an early onset mood disorder and history of suicidal behavior. These high-risk individuals were subdivided into the following groups: high risk resilient having no mood disorder or suicidal behavior (n = 29); high risk with mood disorder and no suicidal behavior history (n = 31); and high risk with mood disorder and suicidal behavior (n = 25). Groups were compared with healthy volunteers without a family history of mood disorder or suicidal behavior (n = 34). Participants underwent the Trier Social Stress Task (TSST). All participants were free from psychotropic medications at the time of the TSST and PET scanning.

RESULTS

We observed no group differences in 5-HT1A receptor binding considering all regions simultaneously, nor did we observe heterogeneity of the effect of group across regions. These results were similar across outcome measures (BPND for all participants and BPp in a subset of the sample) and definitions of regions of interest (ROIs; standard or serotonin system-specific ROIs). We also found no group differences on TSST outcomes. Within the high risk with mood disorder and suicidal behavior group, lower BPp binding (β = -0.084, SE = 0.038, P = .048) and higher cortisol reactivity to stress (β = 9.25, 95% CI [3.27,15.23], P = .004) were associated with higher lethality attempts. There were no significant relationships between 5-HT1A binding and cortisol outcomes.

CONCLUSIONS

5-HT1A receptor binding in ROIs was not linked to familial risk or resilience protecting against suicidal behavior or mood disorder although it may be related to lethality of suicide attempt. Future studies are needed to better understand the biological mechanisms implicated in familial risk for suicidal behavior and how hypothalamic-pituitary-adrenal axis function influences such risk.

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects.Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF.The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling.

Validation of kinetic modeling of [15O]H2O PET using an image derived input function on hybrid PET/MRI

In present study we aimed to validate the use of image-derived input functions (IDIF) in the kinetic modeling of cerebral blood flow (CBF) measured by [¹⁵O]H₂O PET by comparing with the accepted reference standard arterial input function (AIF). Additional comparisons were made to mean cohort AIF and CBF values acquired by methodologically independent phase-contrast mapping (PCM) MRI. Using hybrid PET/MRI an IDIF was generated by measuring the radiotracer concentration in the internal carotid arteries and correcting for partial volume effects using the intravascular volume measured from MRI-angiograms. Seven patients with carotid steno-occlusive disease and twelve healthy controls were examined at rest, after administration of acetazolamide, and, in the control group, during hyperventilation. Agreement between the techniques was examined by linear regression and Bland-Altman analysis. Global CBF values modeled using IDIF correlated with values from AIF across perfusion states in both patients (p<10^-6, R²=0.82, 95% limits of agreement (LoA)=[-11.3-9.9] ml/100 g/min) and controls (p<10^-6, R²=0.87, 95% LoA=[-17.1-13.7] ml/100 g/min). The reproducibility of gCBF using IDIF was identical to AIF (15.8%). Values from IDIF and AIF had equally good correlation to measurements by PCM MRI, R²=0.86 and R²=0.84, (p<10^-6), respectively. Mean cohort AIF performed substantially worse than individual IDIFs (p<10^-6, R²=0.63, LoA=[-12.8-25.3] ml/100 g/min). In the patient group, use of IDIF provided similar reactivity maps compared to AIF. In conclusion, global CBF values modeled using IDIF correlated with values modeled by AIF and similar perfusion deficits could be established in a patient group.

Examining the underpinnings of loudness dependence of auditory evoked potentials with positron emission tomography

Loudness dependence of auditory evoked potentials (LDAEP) has long been considered to reflect central basal serotonin transmission. However, the relationship between LDAEP and individual serotonin receptors and transporters has not been fully explored in humans and may involve other neurotransmitter systems. To examine LDAEP's relationship with the serotonin system, we performed PET using serotonin-1A (5-HT_1A) imaging via [¹¹C]CUMI-101 and serotonin transporter (5-HTT) imaging via [¹¹C]DASB on a mixed sample of healthy controls (n ​= ​4: 4 females, 0 males), patients with unipolar (MDD, n ​= ​11: 4 females, 7 males) and bipolar depression (BD, n ​= ​8: 4 females, 4 males). On these same participants, we also performed electroencephalography (EEG) within a week of PET scanning, using 1000 ​Hz tones of varying intensity to evoke LDAEP. We then evaluated the relationship between LDAEP and 5-HT_1A or 5-HTT binding in both the raphe (5-HT_1A)/midbrain (5-HTT) areas and in the temporal cortex. We found that LDAEP was significantly correlated with 5-HT_1A positively and with 5-HTT negatively in the temporal cortex (p ​< ​0.05), but not correlated with either in midbrain or raphe. In males only, exploratory analysis showed multiple regions in which LDAEP significantly correlated with 5-HT_1A throughout the brain; we did not find this with 5-HTT. This multimodal study partially validates preclinical models of a serotonergic influence on LDAEP. Replication in larger samples is necessary to further clarify our understanding of the role of serotonin in perception of auditory tones.

Prediction of lithium treatment response in bipolar depression using 5-HTT and 5-HT1A PET

BACKGROUND

Lithium, one of the few effective treatments for bipolar depression (BPD), has been hypothesized to work by enhancing serotonergic transmission. Despite preclinical evidence, it is unknown whether lithium acts via the serotonergic system. Here we examined the potential of serotonin transporter (5-HTT) or serotonin 1A receptor (5-HT_1A) pre-treatment binding to predict lithium treatment response and remission. We hypothesized that lower pre-treatment 5-HTT and higher pre-treatment 5-HT_1A binding would predict better clinical response. Additional analyses investigated group differences between BPD and healthy controls and the relationship between change in binding pre- to post-treatment and clinical response. Twenty-seven medication-free patients with BPD currently in a depressive episode received positron emission tomography (PET) scans using 5-HTT tracer [¹¹C]DASB, a subset also received a PET scan using 5-HT_1A tracer [¹¹C]-CUMI-101 before and after 8 weeks of lithium monotherapy. Metabolite-corrected arterial input functions were used to estimate binding potential, proportional to receptor availability. Fourteen patients with BPD with both [¹¹C]DASB and [¹¹C]-CUMI-101 pre-treatment scans and 8 weeks of post-treatment clinical scores were included in the prediction analysis examining the potential of either pre-treatment 5-HTT or 5-HT1A or the combination of both to predict post-treatment clinical scores.

RESULTS

We found lower pre-treatment 5-HTT binding (p = 0.003) and lower 5-HT_1A binding (p = 0.035) were both significantly associated with improved clinical response. Pre-treatment 5-HTT predicted remission with 71% accuracy (77% specificity, 60% sensitivity), while 5-HT_1A binding was able to predict remission with 85% accuracy (87% sensitivity, 80% specificity). The combined prediction analysis using both 5-HTT and 5-HT_1A was able to predict remission with 84.6% accuracy (87.5% specificity, 60% sensitivity). Additional analyses BPD and controls pre- or post-treatment, and the change in binding were not significant and unrelated to treatment response (p > 0.05).

CONCLUSIONS

Our findings suggest that while lithium may not act directly via 5-HTT or 5-HT_1A to ameliorate depressive symptoms, pre-treatment binding may be a potential biomarker for successful treatment of BPD with lithium.

CLINICAL TRIAL REGISTRATION

PET and MRI Brain Imaging of Bipolar Disorder Identifier: NCT01880957; URL: https://clinicaltrials.gov/ct2/show/NCT01880957.