Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Mar 1.
Published in final edited form as: Psychiatry Res. 2024 Jan 26;333:115747. doi: 10.1016/j.psychres.2024.115747

Neural markers of mania that distinguish inpatient adolescents with bipolar disorder from those with other psychopathology

Michele A Bertocci 1, Renata Rozovsky 1, Maria Wolfe 2, Halimah Abdul-waalee 2, Mariah Chobany 2, Greeshma Malgireddy 2, Jonathan A Hart 2, Alex Skeba 2, Tyler Brady 2, Brianna Lepore 2, Amelia Versace 1, Henry W Chase 1, Boris Birmaher 1,2, Mary L Phillips 1, Rasim S Diler 1,2
PMCID: PMC10922873  NIHMSID: NIHMS1963709  PMID: 38301286

Abstract

Pediatric bipolar disorder (BD) is difficult to distinguish from other psychiatric disorders, a challenge which can result in delayed or incorrect interventions. Using neuroimaging we aimed to identify neural measures differentiating a rarified sample of inpatient adolescents with BD from other inpatient psychopathology (OP) and healthy adolescents (HC) during a reward task. We hypothesized reduced subcortical and elevated cortical activation in BD relative to other groups, and that these markers will be related to self-reported mania scores. We examined inpatient adolescents with diagnosis of BD-I/II (n=29), OP (n=43), and HC (n=20) from the Inpatient Child and Adolescent Bipolar Spectrum Imaging study. Inpatient adolescents with BD showed reduced activity in right thalamus, left thalamus, and left amygdala, relative to inpatient adolescents with OP and HC. This reduced neural function explained 21% of the variance in past month and 23% of the variance in lifetime mania scores. Lower activity in regions associated with the reward network, during reward processing, differentiates BD from OP in inpatient adolescents and explains >20% of the variance in mania scores. These findings highlight potential targets to aid earlier identification of, and guide new treatment developments for, pediatric BD.

Keywords: bipolar disorder, adolescent, mania, reward, mri

1. Introduction

Bipolar Disorder (BD) is a serious, recurrent illness that often emerges during adolescence (Kowatch, et al., 2005a, Kowatch, et al., 2005b, Pavuluri, et al., 2005) and its primary distinguishing feature is elevated symptoms of mania. Fifteen to twenty eight percent of adults with BD experience illness onset before age 13 years and 50–66% before age 19 (Leverich, et al., 2002, Leverich, et al., 2003, Perlis, et al., 2004). Approximately 5.6% of adolescents have threshold/subthreshold manic, hypomanic, or depressive symptoms and these symptoms of BD overlap with other disorders, such as unipolar depressive disorders, Attention Deficit/Hyperactive Disorder (ADHD), or Disruptive Behavior Disorders, making it difficult to diagnose BD (Birmaher and Axelson, 2006, Lewinsohn, et al., 1995) and resulting in about 7–10 years of delayed correct diagnosis and risk for inappropriate treatment (Bolge, et al., 2008, Leverich, et al., 2007). Even among severely ill inpatient adolescents, diagnosis of BD is often misidentified. It is thus important to identify objective biological markers that are specific to BD and its manic features with the goal to differentiate BD from other disorders and to aid in early identification and effective treatment choices.

The Inpatient Child and Adolescent Bipolar Spectrum Imaging study (InCabs Imaging) is an ongoing study of inpatient adolescents who are carefully assessed over the inpatient stay with comprehensive evaluation using the Schedule for Affective Disorders and Schizophrenia for School-Aged Children-Present and Lifetime (KSADS-PL) (Kaufman, et al., 1997) based on Diagnostic and Statistical Manual 5 (DSM-5) (American Psychological Association, 1994), daily check-ins by research staff, daily mood ratings and actigraphy, and MRI. Study participants were diagnosed with either well-characterized bipolar I/II disorder (BD) or with other psychopathology (OP) without any threshold/subthreshold mania or hypomania, and these diagnoses include unipolar depressive disorders, ADHD, anxiety disorders, and disruptive behavior disorders. Importantly, OP participants and their parents/guardians report a complete absence of any current or lifetime mania, hypomania, and subthreshold mania/hypomania symptoms. This unique and rarified of sample of very well-characterized youth with severe pathology provides a promising opportunity to identify a neural marker specific to mania. Given that some mania related symptoms of BD, such as increased goal directed and risk taking behaviors are related to reward processing, and previous studies have suggested dysfunction in reward circuitry, reward processing may be a constructive approach to distinguish BD from other psychopathologies (Frazier, et al., 2005, Nusslock, et al., 2014). This study aims to identify objective neural markers that distinguish BD from OP by comparing reward related neural circuitries in these two groups, and relative to healthy youth, during a monetary reward task. The following description of reward circuitry informs our understanding of reward processing. Studies with humans and nonhuman primates have established that reward circuitry comprises a interconnected network of fronto-subcortical regions connected via the thalamus (Haber and Knutson, 2010, Russo and Nestler, 2013) and these pathways interface with the motor cortex for motor planning (Haber and Knutson, 2010). The ventral (Grabenhorst and Rolls, 2011, Rolls, et al., 2022), medial (Rolls, et al., 2022), and orbital (Walton, et al., 2011) portions of the frontal cortex (Haber and Knutson, 2010) encode the value of and features of different reward types, while the anterior cingulate cortex (ACC) is involved in reward based decisions (Bush, et al., 2002). Ventral striatum (VS) is associated with prediction and prediction error (Haber, 2011) and is critically connected with amygdala for stimulus-reward associations (Haber and Knutson, 2010). These functions of regions during reward reflect symptoms and may differentiate BD from OP.

While no studies to our knowledge have striven to differentiate well characterized inpatient adolescents with BD from inpatients with other psychopathology, studies in outpatient samples have shown the reward circuitry to be differentially implicated in BD relative to unipolar depression, although the directions of the relationships are inconsistent. Relative to unipolar depressed youth, BD youth showed a pattern of elevated orbitofrontal cortex (OFC), ventrolateral prefrontal cortex (vlPFC) (Chase, et al., 2013), ACC (Chase, et al., 2013, Kollmann, et al., 2017), and VS (Dutra, et al., 2015) activity along with reduced activity in thalamus (Chase, et al., 2013, Trost, et al., 2014), precentral gyrus (Chase, et al., 2013, Redlich, et al., 2015), and striatum/nucleus accumbens (Abler, et al., 2008, Johnson, et al., 2019, Redlich, et al., 2015, Schreiter, et al., 2016, Trost, et al., 2014, Yip, et al., 2015). While other pediatric studies have shown no reward related differences between BD and unipolar depression (Long, et al., 2022, Satterthwaite, et al., 2015, Sharma, et al., 2016, Wakatsuki, et al., 2022). Relative to healthy participants, BD have shown reduced activities in thalamus (Redlich, et al., 2015, Singh, et al., 2013), inferior temporal gyrus (Singh, et al., 2013), and frontal cortex (Long, et al., 2022, Redlich, et al., 2015). These discrepancies may be due to diagnostic inconsistencies or clinical features of BD such as medication, affective lability, or emotional dysregulation that may not be related to mania.

Given studies showing differences in reward related neural circuitry in BD relative to unipolar depressed youth shown above, we hypothesized, reduced subcortical activation, and elevated cortical activation in BD relative to OP. Additionally, given the specific characteristics of our recruitment, it was hypothesized that these neural activity markers of reward that differentiate BD will be related to mania ratings but not to other symptom measures.

2. Methods

Participants

One hundred and twenty-two adolescents were recruited for this study. There were no differences in age (p=0.084) or race (p=0.086) among the inpatient sample with BD-I/II (n= 38), inpatient sample with OP (n=60), and HC (n=24). There was a greater proportion of female participants recruited in the BD and OP compared to HC (p=0.012). Table 1.

Table 1.

Demographics table comparison across groups.

BD OP HC
n=38 n=60 n=24
mean SD mean SD mean SD statistic p value
Age 15.5 1.2 15.1 1.2 14.8 1.5 F= 2.66 0.084
Sex at birth (F) 31 42 11 x2= 8.82 0.012
Race x2= 11.09 0.085
Black 3 21 6
White 34 37 18
Other 1 2 0
Hispanic (yes) 4 2 2 x2= 6.28 0.179
BCMRS past month 12.2 5.7 9.1 5.3 2.1 2.5 F= 56.72 <0.001
BCMRS lifetime 14.8 5.1 9.3 5.1 2.7 2.7 F= 53.14 <0.001
SCARED 47.7 19.8 37.0 20.6 9.7 6.4 F= 82.43 <0.001
MFQ 43.4 15.3 36.9 16.4 3.8 4.4 F= 140.02 <0.001
PANAS negative 48.8 15.1 42.5 15.8 19.2 5.0 F= 83.84 <0.001
PANAS positive 36.9 10.3 33.3 12.5 46.7 7.5 F= 14.96 <0.001
Open in a new tab

All clinical measures are self-reported by the participant. Other race includes Asian, American Indian/Alaska Native, and Native Hawaiian. Abbreviations: BD = bipolar disorder group, OP = other psychopathology group, HC = healthy control group, BCMRS = Brief Child Mania Rating Scale, MFQ= Mood and Feelings Questionnaire, PANAS = Positive and Negative Affect Scale, SD = standard deviation.

After quality control of neuroimaging data and missing clinical scales (see below for criteria), 92 participants were included in this analysis. Included participants did not differ on age (p= 0.224) or race (p=0.233). Inpatient BD, n=29, age=15.48 (1.09), 23 female, 3 African American, 25 White, 1 Other race; and inpatient OP, n=43, age=15.19 (1.28), 30 female, 15 African American, 26 White, 2 Other race; and HC, n=20, age=14.95 (1.67), 9 female, 4 African American, 16 White. Included participants differed on sex at birth (p=0.038) with a greater proportion of female participants in the BD and OP groups.

Exclusion criteria included: history of serious medical illness, head injury, or neurological disorder; IQ<70, assessed with Wechsler Abbreviate Scale of Intelligence (Wechsler, 1999); diagnosis of autism, eating disorder, schizophrenia, or severe substance use disorder, and magnetic resonance imaging (MRI) contraindication (e.g., pregnancy, metal in the body). For HC, additional exclusion criteria included history of any DSM-5 disorder. This study was approved by the Human Research Protection Office at the University of Pittsburgh. Before participation, parents/guardians provided informed consent, and adolescents provided informed assent. Participants received monetary compensation.

All clinical diagnostic staff were directly trained by, and all psychiatric diagnoses were confirmed through consensus meetings by Rasim Somer Diler, MD, a licensed, board certified, pediatric psychiatrist specializing in pediatric bipolar disorder and director of the Inpatient Child and Adolescent Bipolar Services (InCabs) unit at Western Psychiatric Hospital, University of Pittsburgh Medical Center (UPMC). All instruments had good psychometric properties, included the Kiddie Schedule for Affective Disorders and Schizophrenia for School-Age Children (K-SADS) (Kaufman, et al., 1997), self-report measures of symptoms included the Brief Child Mania Rating Scale (Henry, et al., 2008, Pavuluri, et al., 2006) to measure mania in the past month and lifetime, the Screen for Child Anxiety Related Disorders (SCARED) to measure anxiety (Birmaher, et al., 1997, Birmaher, et al., 1999), the Mood and Feelings Questionnaire (MFQ) to measure depression (Wood, et al., 1995), and the Positive and Negative Affect Scale (PANAS) (Laurent, et al., 1999) to measure positive affect and negative affect.

All inpatient participants were medicated and receiving inpatient treatment.

Neuroimaging Data Acquisition

fMRI Acquisition Parameters

Functional neuroimaging data were collected at the University of Pittsburgh using a 3.0 Tesla Siemens Magnetom Prisma MRI scanner. Blood-oxygenation-level-dependent (BOLD) images were acquired with a multi-band gradient echo EPI sequence (72 slices, 2mm isotropic voxels; TR=800ms, TE=30ms; field of view=210 × 210mm; flip angle 52°, bandwidth 2290 Hz Px). Structural sagittal MPRAGE images (TR=2300ms, TE=2.9ms; flip angle 9° FOV=256 × 256 mm; 1 mm isotropic voxels; 176 continuous slices).

Monetary Reward Paradigm

Win trials comprised expectation of a win followed by a win outcome or no change; loss trials comprised expectation of a loss followed by a loss or no change; mixed trials comprised expectation of either win or loss, followed by win or loss; and neutral trials had no expectation of either win or loss, followed by no change. Each trial included a virtually presented card, participants then guessed by button press whether the card’s value was higher or lower than “5” (4 seconds). A jittered 2–6s expectancy cue occurred followed by accuracy feedback. An outcome cue was then presented for 1 second followed by an intertrial interval of 0.5–1.5 seconds. Participants completed one 8-minute block of 48 trials (12 per trial type) randomized with predetermined outcomes.

Reward expectancy (RE), reward prediction error (RPE), and outcome expectancy (OE) were derived from the monetary values associated with each trial type. RPE was calculated as the difference between the expected and outcome reward values for each trial type: +$0.50 for a win and −$0.50 for no win in the possible win condition; +$0.375 for a no loss and −$0.375 for a loss in the possible loss condition; +$0.875 for a win and −$0.875 for a loss in the mixed condition and zero in the neutral condition. Reward expectancy (RE), the expected value of a potential reward, and outcome expectancy (OE), the anticipated outcome of a trial, were calculated during the reward anticipation period in each trial. RE was defined as the expected value of the arrow: +$0.50 for the possible win condition (50% chance of winning $1), −$0.375 for the possible loss condition (50% chance of losing $0.75), +$0.125 for the mixed condition (50% chance of winning $1; 50% chance of losing $0.75), and zero for the neutral condition. In contrast, OE represented the range of unsigned values of possible outcomes where the greatest value was for the mixed trials ($1−$0.75 = 1.75) and lowest for neutral trials (zero). Possible win ($1−$0 = 1) and possible loss (0−$0.75 = 0.75). Our contrast of interest was RE versus all other task conditions.

Neuroimaging Data Processing

Results included in this manuscript come from preprocessing performed using *fMRIPrep* 20.2.1 (@fmriprep1; @fmriprep2; RRID:SCR_016216), which is based on *Nipype* 1.5.1 (@nipype1; @nipype2; RRID:SCR_002502). See supplement for full anatomical and functional data processing details.

Many internal operations of *fMRIPrep* use *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362], mostly within the functional processing workflow. For more details of the pipeline, see [the section corresponding to workflows in *fMRIPrep*’s documentation] (https://fmriprep.readthedocs.io/en/latest/workflows.html “FMRIPrep’s documentation”).

Neuroimaging Data Analysis

For first level analyses, Our contrast of interest was reward expectancy (RE) versus all other task conditions in order to control for other neural activation elicited by the task (see paradigm description above). Physiologic fluctuations with the mean signal in CSF, white matter, and high deviation voxels were determined with CompCor(Behzadi, et al., 2007) and entered as a covariate to reduce noise. Motion parameters during scanning were entered as covariates to control for head movement. A 60s high-pass filter and autoregressive modelling were implemented during fitting.

At the second level, using Statistical Parametric Mapping (SPM) 12, we examined the main effect of task (RE versus all other task conditions). This contrast activated the expected reward related regions at the whole brain level (Supplement Figure 1). To identify activity to this contrast that would differentiate BD, OP, and HC, our primary test of interest was main effect of group activity; we used one large anatomical mask consisting of reward related regions(Chase, et al., 2013, Haber, 2011, Haber and Knutson, 2010, Rolls, et al., 2022, Walton, et al., 2011): brainstem, bilateral amygdala, accumbens, caudate, putamen, thalamus, ACC, medial, orbital, and lateral frontal cortices, and the motor cortex created from the Neuromorphometrics atlas in SPM12. We extracted parameter estimates from the significant regions (p<0.001, k>15) (Figure 1). ANOVAs and post-hoc t-tests quantified the between-group differences in the neuroimaging measures identified in SPM, Bonferroni corrected (p= 0.05/4 regions = 0.013). Post hoc power calculations were computed.

Figure 1.

Figure 1.

Open in a new tab

Main effect of group activity to parameterized reward within the reward network ROI. Panel A. SPM peak-level small volume corrected statistics for each region of activity that differs across groups. Panel B. Representative images of regions of activity that differ across groups. Panel C. Clustered error bars of each region of activity with statistics, p values, and an explanatory measure of effect size: ξ. Values of ξ = 0.10= small effect size, 0.30= medium effect size, and 0.50= large effect size (Mair and Wilcox, 2020). Abbreviations: bipolar disorder group = BD; other psychopathology group = OP; healthy control group = HC

After testing the assumptions of regression and assessing the correct regression family, regression was used to examine relationships among the significant neural regions identified in SPM that differentiate BD, OP, and HC with symptom measures. We used the WRS2 package in R which is robust to assumption deviations (Mair and Wilcox, 2020). Full models were Bonferroni corrected (p= 0.05/6 symptom measure tests = 0.008). Bootstrapped confidence intervals were used to clarify within test coefficients and False Discovery Rate (FDR) correction was used to correct for multiple post hoc t-tests.

To test medication effects, we used t-tests (on/off classes of medication) comparing activity in each region for BD and OP. See supplement for list of medications in each medication class (including: lithium, antipsychotic, anticonvulsant, stimulant, non-stimulant ADHD medication, antidepressant, antianxiety).

Role of the funding source

The funder had no role in study design, data collection, data analysis, data interpretation, or writing of the report.

3. Results

SPM analysis revealed a main effect of group (BD vs OP vs HC) in left amygdala (F(2,89)=10.06, ZE=3.69, k=16, p<0.001), left thalamus (F(2,89)=11.00, ZE=3.87, k=15, p<0.001), and two cluster in right thalamus (F(2,89)=14.18, ZE=4.44, k= 31, p<0.001) and (F(2,89)=9.28, ZE=3.52, k=18, p<0.001). ANOVA of extracted SPM parameter estimates for these four regions, showed significant effects across groups with large effect sizes (Figure 1). Between group comparisons showed significantly reduced activity in left amygdala (BD mean= −0.20 (1.1)) relative to OP (mean=0.72 (0.96)) and HC (mean=0.85 (1.1)) (FDR corrected p=0.006 and p=0.006, respectively), OP and HC did not differ (p= 0.39). BD (mean= −0.18 (1.4)) showed reduced activity in left thalamus relative to OP (mean=0.45 (1.2)) and HC (mean=1.6 (1.5)) (FDR corrected p=0.067 and p=0.006 respectively), OP left thalamus activity was lower than HC (p=0.051). BD showed reduced right thalamus activity in two clusters with (BD mean= 0.05 (0.62)), relative to (OP mean=0.63 (1.1)) and HC (mean=1.7 (1.1)) (FDR corrected p=0.024 and p=0.002 respectively), OP was reduced relative to HC (p= 0.024). In the second right thalamus cluster, BD showed reduced activity (BD mean= −0.40 (1.5)), relative to OP (mean=0.92 (1.3)) (FDR corrected p=0.024 but trend reduced from HC (mean=0.35 (1.1)) FDR corrected p=0.093; OP and HC did not differ (FDR corrected p=0.382) Figure 1. Post hoc power calculations showed that using ANOVA with Bonferroni correction to p=0.01, we had 81% to 97% power to detect a root-mean-square standardized (RMSSE) medium to large effects (0.47–0.73) with our sample sizes (Table 3)

Table 3.

Post analysis power calculations for each region.

Test Assumptions
Region Powerb Nc Std. Dev. Effect Sized Sig.
Left Amygdala Overall Testa 0.815 92 1.11 0.516 0.01
Left Thalamus Overall Testa 0.886 92 1.46 0.627 0.01
Right Thalamus Overall Testa 0.967 92 1.17 0.725 0.01
Right Thalamus Overall Testa 0.812 92 1.42 0.473 0.01
Open in a new tab
a.

Test the null hypothesis that population mean is the same for all groups.

b.

Based on noncentral F-distribution.

c.

Total sample size across groups.

d.

Effect size measured by the root-mean-square standardized effect.

Linear regression model assumptions were not met for self-report of lifetime and past month mania symptoms (BCMRS), anxiety symptoms (SCARED), and positive and negative affect scores (PANAS), we transformed these DVs using square root transformation. Linear model assumptions were met for depression scores (MFQ).

Regression models showed reward related activity in these four regions explained 21% of the variance in past month mania (BCMRS) score F(4, 75)=4.84, p<0.002, and 23% of the variance in lifetime mania (BCMRS) score F(4,74) = 5.38, p<0.001. Activity in these four reward relate neural regions did not explain depression (MFQ) score F(4, 86)=0.59, p=0.665; anxiety (SCARED) score F(4, 74)=1.61, p=0.181; negative affect (PANAS negative) score F(4, 72)= 1.19, p=0.323; or positive affect (PANAS positive) score F(4,70)=0.51, p=0.729. Table 2.

Table 2.

Results of regression models with symptom measures. Abbreviations: BCMRS = Brief child mania rating scale, MFQ= Mood and feelings questionnaire, PANAS = Positive and Negative affect scale. Bonferroni correction for full models (p= 0.05/6 symptom measure tests = 0.008).

Bootstrapped 95% CI
DV IV t sig Beta Sig. (2-tailed) Lower Upper R R Square F Sig.
BCMRS past month Amygdala left −2.91 0.005 −0.33 0.004 −0.59 −0.11 0.45 0.21 4.84 0.002
Thalamus left −0.78 0.440 −0.008 0.067 −0.47 0.02
Thalamus right −1.81 0.073 −0.21 0.378 −0.37 −0.14
Thalamus right 0.63 0.531 0.06 0.292 −0.10 0.31
BCMRS lifetime Amygdala left −3.26 0.002 −0.35 0.009 −0.53 −0.08 0.48 0.23 5.38 0.001
Thalamus left −0.45 0.654 −0.04 0.224 −0.37 0.09
Thalamus right −1.98 0.050 −0.21 0.100 −0.48 0.04
Thalamus right 0.00 0.999 0.00 0.788 −0.17 0.22
MFQ Amygdala left −0.51 0.611 −16.28 0.457 −73.18 33.23 0.17 0.03 0.60 0.665
Thalamus left −0.96 0.341 −25.37 0.021 −110.13 −9.24
Thalamus right 0.27 0.791 8.10 0.145 −14.95 99.59
Thalamus right −0.39 0.699 −9.55 0.185 −74.12 14.50
SCARED Amygdala left −0.88 0.384 −0.18 0.366 −0.58 0.22 0.28 0.08 1.61 0.181
Thalamus left −0.37 0.713 −0.07 0.122 −0.72 0.09
Thalamus right −1.67 0.099 −0.36 0.212 −0.73 0.16
Thalamus right −0.01 0.989 0.00 0.544 −0.25 0.46
Positive PANAS Amygdala left 0.01 0.992 0.00 0.178 −0.06 0.33 0.17 0.03 0.51 0.729
Thalamus left −0.26 0.793 −0.02 0.543 −0.27 0.14
Thalamus right 1.22 0.226 0.14 0.169 −0.08 0.43
Thalamus right 0.40 0.688 0.04 0.580 −0.13 0.24
Negative PANAS Amygdala left −0.72 0.504 −0.10 0.120 −0.51 0.06 0.25 0.06 1.19 0.323
Thalamus left 0.42 0.241 −0.15 0.141 −0.52 0.08
Thalamus right −1.85 0.424 0.12 0.827 −0.41 0.33
Thalamus right −0.04 0.395 0.11 0.169 −0.08 0.46
Open in a new tab

T-tests showed that activity in each neural region did not differ by being on vs. off any class of medication for the BD and the OP participants, all FDR correction is non-significant.

4. Discussion

To our knowledge, this is the first study to compare neural circuitry in inpatient adolescents with well-characterized diagnoses of BD-I/II and OP in an effort to differentiate these groups and to identify a neural marker specific to mania. The ongoing InCabs Imaging study with its unique comprehensive multi-day observations and evaluations with multiple reporters, provides a promising opportunity to identify neural activity that differs between samples of severely ill inpatient adolescents with BD and severely ill inpatient adolescents without a history of mania-like symptoms. Using a monetary reward task, our main finding was reduced activity in the reward network in BD relative to OP and HC with large effect sizes, and supports dysfunction in the reward network (Frazier, et al., 2005). Specifically, BD showed lower reward-related activity in left amygdala, left thalmus, and right thalamus, relative to both OP and HC. Activity in these neural regions was associated with self-report ratings of past month mania and lifetime mania but not to self-report ratings of depression, anxiety, positive affect, or negative affect which may suggest that this neural dysfunction is directly related to manic symptoms.

These results are in line with other studies of severely ill bipolar youth where reduced thalamus activity to reward has been shown to differentiate BD from HC (Redlich, et al., 2015, Singh, et al., 2013) and BD from ADHD when attention is required (Patino, et al., 2023). The role of the thalamus as the bidirectional neural relay between subcortical and frontal cortex, has been shown in human and nonhuman primate studies (Haber, 2011, Haber and Knutson, 2010, Russo and Nestler, 2013) and higher thalamus activation is associated with increased arousal to reward (Haber and Knutson, 2010). Thus, our finding in BD of lower thalamus activation may suggest dysfunction in relaying reward related information, possibility resulting in lower and unsatisfying arousal to this monetary reward task. This potential lack of satisfying arousal response may also be supported by our finding of reduced amygdala activity in BD relative to OP and HC. Amygdala plays a key role in arousal and emotional coding, and a reduction in amygdala activity over time has been shown with devaluing of a reward (Gottfried, et al., 2003), amygdala habituation to rewarding stimuli (Breiter, et al., 1996, Gottfried, et al., 2003), and increases in amygdala activity with changes in stimulus rules (Linke, et al., 2012). While both amygdala hypoactivation (Berghorst, et al., 2016) and hyperactivation (Berghorst, et al., 2016, Simonetti, et al., 2022) have been reported in BD, our finding of reduced thalamus and amygdala activation together, may point to dysfunction in arousal and emotional response to rewarding stimuli (Frazier, et al., 2005, Haber and Knutson, 2010). A lack of arousal may suggest that a monetary reward task is insufficient to satisfy the risk taking drive in inpatient adolescents with BD.

A large portion of the variance in self-reported past month mania scores (21%) and lifetime mania scores (23%) was associated with the identified reward network dysfunction, while other self-reported symptom measures were not associated with this neural activity. These relationships with past month and lifetime mania were driven most strongly by reduced left amygdala activity potentially suggesting a trait marker of reduced arousal to monetary reward with elevated mania. Thus, this pattern of reduced subcortical neural activity could potentially be a distinguishing feature of BD. However, the consistency of these relationships over the two time points is notable and is likely related to the strong positive correlation between past month and lifetime mania scores (r=.81). Given that the participants are adolescents and potentially having had experienced only a few manic episodes, both timepoints may be largely reflecting the most recent manic episode, that potentially precipitated the inpatient stay. Future studies could more carefully examine the timeframes of the self-reported lifetime mania scores.

It is also notable that while there was a main effect of task in VS activity (see supplemental Figure 1), we did not identify VS activity as distinguishing our three groups. This is consistent with a recent meta-analysis comparing BD and HC reward circuitry (Long, et al., 2022) however, VS activity has been shown to be elevated in impulsive and risky young adults (Chase, et al., 2017), a hallmark symptoms of BD. Our failure to identify VS activity as differentiating BD from OP may be related to the inclusion of OP inpatient youth with disruptive behavior disorders and ADHD both of whom also suffer from impulsivity and a tendency toward risky behaviors that possibly was related to the need for their inpatient admissions. A large proportion (40.5%) of our OP sample had ADHD or a disruptive behavior disorder. Future studies should examine this possibility.

Studies have also shown significant differences between BD and healthy in ACC and prefrontal cortex (Chase, et al., 2013) yet, although there was significant prefrontal activity across groups during the task (see supplemental Figure 1), our results did not show that these regions distinguish our three adolescent groups during task performance.

This study had limitations. Sample size was limited however post hoc power analysis indicated sufficient power to detect our effects (Table 3). In addition, data on inpatient adolescents with multi-day observation resulting in well-defined diagnosis is rare and of significant clinical and scientific importance. Medication use may have impacted some findings; however, neural activity did not differ between those on vs. off any class of medication, and inpatients who participated in the study, in both BD and OP groups, were receiving both medication and therapy interventions. There were also more inpatient females in our sample than males. Given the naturalistic nature of recruitment for this study, this reflects the larger proportion of female inpatients generally (Hayes, et al., 2023).

To our knowledge, this is the first study to compare inpatient adolescents with well characterized multi-day evaluations of symptoms diagnosed with BD or OP. Specifically we show that lower reward network activity during reward processing differentiates BD from OP. This activity also predicts more than 20% of past month self-reported mania severity and lifetime self-reported mania severity and are thus candidate objective state and trait markers of pediatric BD that may be incorporated into diagnostic risk calculator models. Furthermore, these neural regions have been shown to be amenable to training such as neurofeedback (Herwig, et al., 2019, Young, et al., 2014) and the potential for neurostimulation (Inman, et al., 2020, Sellers, et al., 2024) in other psychiatric conditions suggesting that our findings are an important step toward identifying neural markers specific to mania to aid in early accurate identification of, and to guide appropriate interventions for, pediatric BD.

Supplementary Material

1

Highlights.

  • Reward activity differentiates inpatient adolescents with bipolar disorder from other psychopathologies.

  • Reward related activity associated with symptoms of mania.

Acknowledgements

We would like to acknowledge and thank the participants and their families for their contributions to this study.

Funding

National Institute of Mental Health, Brain and Behavior Research Foundation, and National Institute of Health

Financial Support

This work was supported by the National Institute of Mental Health (MAB and RSD, R01-MH-121451, From Manic Symptoms to Bipolar: Neural Behavioral Markers Using Two Analytical Models) and Brain and Behavior Research Foundation (MAB). This research was supported in part by the University of Pittsburgh Center for Research Computing, RRID:SCR_022735, through the resources provided. Specifically, this work used the HTC cluster, which is supported by NIH award number S10OD028483.

Disclosures of Interest

Bertocci, Rozovsky, Abdul-waalee, Chobany, Malgireddy, Hart, Skeba, Brady, Lepore, Verace, Chase, Phillips, Diler have no financial interests or potential conflicts of interest. Birmaher has or will receive royalties from for publications from Random House, Inc (New hope for children and teens with bipolar disorder) and Lippincott Williams & Wilkins (Treating child and adolescent depression). He is employed by the University of Pittsburgh and the University of Pittsburgh Medical Center and receives research funding from NIMH.

The funding agency was not involved in the conduct, analysis, or reporting of this work.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Ethical Standards

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

References

  1. Abler B, Greenhouse I, Ongur D, Walter H and Heckers S, 2008, Abnormal reward system activation in mania, Neuropsychopharmacology, 33, 2217–2227, 10.1038/sj.npp.1301620. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. American Psychological Association, 1994, Diagnostic and statistical manual of mental disorders, Fifth.
  3. Behzadi Y, Restom K, Liau J and Liu TT, 2007, A component based noise correction method (CompCor) for BOLD and perfusion based fMRI, Neuroimage, 37, 90–101, 10.1016/j.neuroimage.2007.04.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Berghorst LH, Kumar P, Greve DN, Deckersbach T, Ongur D, Dutra SJ and Pizzagalli DA, 2016, Stress and reward processing in bipolar disorder: a functional magnetic resonance imaging study, Bipolar Disorders, 18, 602–611, 10.1111/bdi.12444. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Birmaher B, Khetarpal S, Brent D, Cully M, Balach L, Kaufman J and Neer SM, 1997, The Screen for Child Anxiety Related Emotional Disorders (SCARED): scale construction and psychometric characteristics, J Am Acad Child Adolesc Psychiatry, 36, 545–53, 10.1097/00004583-199704000-00018. [DOI] [PubMed] [Google Scholar]
  6. Birmaher B, Brent DA, Chiappetta L, Bridge J, Monga S and Baugher M, 1999, Psychometric properties of the Screen for Child Anxiety Related Emotional Disorders (SCARED): a replication study, J Am Acad Child Adolesc Psychiatry, 38, 1230–6, 10.1097/00004583-199910000-00011. [DOI] [PubMed] [Google Scholar]
  7. Birmaher B and Axelson D, 2006, Course and outcome of bipolar spectrum disorder in children and adolescents: a review of the existing literature, Dev Psychopathol, 18, 1023–35, 10.1017/s0954579406060500. [DOI] [PubMed] [Google Scholar]
  8. Bolge SC, Thompson T, Bourne E and Nanry K, 2008, Characteristics and symptomatology of patients diagnosed with unipolar depression at risk for undiagnosed bipolar disorder: a bipolar survey, CNS spectrums, 13, 216–224, Doi 10.1017/S1092852900028467. [DOI] [PubMed] [Google Scholar]
  9. Breiter HC, Etcoff NL, Whalen PJ, Kennedy WA, Rauch SL, Buckner RL, Strauss MM, Hyman SE and Rosen BR, 1996, Response and habituation of the human amygdala during visual processing of facial expression, Neuron, 17, 875–887, 10.1016/s0896-6273(00)80219-6. [DOI] [PubMed] [Google Scholar]
  10. Bush G, Vogt BA, Holmes J, Dale AM, Greve D, Jenike MA and Rosen BR, 2002, Dorsal anterior cingulate cortex: a role in reward-based decision making, Proceedings of the National Academy of Sciences, 99, 523–528, 10.1073/pnas.012470999. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chase H, Fournier J, Bertocci M, Greenberg T, Aslam H, Stiffler R, Lockovich J, Graur S, Bebko G and Forbes E, 2017, A pathway linking reward circuitry, impulsive sensation-seeking and risky decision-making in young adults: identifying neural markers for new interventions, Translational psychiatry, 7, e1096–e1096, 10.1038/tp.2017.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Chase HW, Nusslock R, Almeida JR, Forbes EE, LaBarbara EJ and Phillips ML, 2013, Dissociable patterns of abnormal frontal cortical activation during anticipation of an uncertain reward or loss in bipolar versus major depression, Bipolar disorders, 15, 839–854, [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dutra SJ, Cunningham WA, Kober H and Gruber J, 2015, Elevated striatal reactivity across monetary and social rewards in bipolar I disorder, Journal of abnormal psychology, 124, 890, 10.1037/abn0000092. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Frazier JA, Ahn MS, DeJong S, Bent EK, Breeze JL and Giuliano AJ, 2005, Magnetic resonance imaging studies in early-onset bipolar disorder: a critical review, Harvard review of psychiatry, 13, 125–140, 10.1080/10673220591003597. [DOI] [PubMed] [Google Scholar]
  15. Gottfried JA, O’Doherty J and Dolan RJ, 2003, Encoding predictive reward value in human amygdala and orbitofrontal cortex, Science, 301, 1104–1107, 10.1126/science.1087919. [DOI] [PubMed] [Google Scholar]
  16. Grabenhorst F and Rolls ET, 2011, Value, pleasure and choice in the ventral prefrontal cortex, Trends in cognitive sciences, 15, 56–67, 10.1016/j.tics.2010.12.004. [DOI] [PubMed] [Google Scholar]
  17. Haber S, 2011, Neuroanatomy of Reward: A view from the ventral striatum, [PubMed]; Gottfried J.Place, Haber SN and Knutson B, 2010, The reward circuit: linking primate anatomy and human imaging, Neuropsychopharmacology, 35, 4–26, 10.1038/npp.2009.129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hayes C, Simmons M, Palmer V, Hamilton B, Simons C and Hopwood M, 2023, Key features of adolescent inpatient units and development of a checklist to improve consistency in reporting of settings, Journal of Psychiatric and Mental Health Nursing, 30, 74–100, 10.1111/jpm.12856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Henry DB, Pavuluri MN, Youngstrom E and Birmaher B, 2008, Accuracy of brief and full forms of the Child Mania Rating Scale, Journal of clinical psychology, 64, 368–381, 10.1002/jclp.20464. [DOI] [PubMed] [Google Scholar]
  20. Herwig U, Lutz J, Scherpiet S, Scheerer H, Kohlberg J, Opialla S, Preuss A, Steiger V, Sulzer J and Weidt S, 2019, Training emotion regulation through real-time fMRI neurofeedback of amygdala activity, NeuroImage, 184, 687–696, 10.1016/j.neuroimage.2018.09.068. [DOI] [PubMed] [Google Scholar]
  21. Inman CS, Bijanki KR, Bass DI, Gross RE, Hamann S and Willie JT, 2020, Human amygdala stimulation effects on emotion physiology and emotional experience, Neuropsychologia, 145, 106722, 10.1016/j.neuropsychologia.2018.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Johnson SL, Mehta H, Ketter TA, Gotlib IH and Knutson B, 2019, Neural responses to monetary incentives in bipolar disorder, NeuroImage: Clinical, 24, 102018, 10.1016/j.nicl.2019.102018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kaufman J, Birmaher B, Brent D, Rao U, Flynn C, Moreci P, Williamson D and Ryan N, 1997, Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL): initial reliability and validity data, J Am Acad Child Adolesc Psychiatry, 36, 980–8, 10.1097/00004583-199707000-00021. [DOI] [PubMed] [Google Scholar]
  24. Kollmann B, Scholz V, Linke J, Kirsch P and Wessa M, 2017, Reward anticipation revisited-evidence from an fMRI study in euthymic bipolar I patients and healthy first-degree relatives, Journal of Affective Disorders, 219, 178–186, 10.1016/j.jad.2017.04.044. [DOI] [PubMed] [Google Scholar]
  25. Kowatch RA, Fristad M, Birmaher B, Wagner KD, Findling RL and Hellander M, 2005a, Treatment guidelines for children and adolescents with bipolar disorder, J Am Acad Child Adolesc Psychiatry, 44, 213–35, 10.1097/00004583-200503000-00006. [DOI] [PubMed] [Google Scholar]
  26. Kowatch RA, Youngstrom EA, Danielyan A and Findling RL, 2005b, Review and meta-analysis of the phenomenology and clinical characteristics of mania in children and adolescents, Bipolar Disord, 7, 483–96, 10.1111/j.1399-5618.2005.00261.x. [DOI] [PubMed] [Google Scholar]
  27. Laurent J, Catanzaro SJ, Joiner TE Jr, Rudolph KD, Potter KI, Lambert S, Osborne L and Gathright T, 1999, A measure of positive and negative affect for children: scale development and preliminary validation, Psychological assessment, 11, 326, Doi 10.1037/1040-3590.11.3.326. [DOI] [Google Scholar]
  28. Leverich GS, McElroy SL, Suppes T, Keck PE Jr., Denicoff KD, Nolen WA, Altshuler LL, Rush AJ, Kupka R, Frye MA, Autio KA and Post RM, 2002, Early physical and sexual abuse associated with an adverse course of bipolar illness, Biol Psychiatry, 51, 288–97, 10.1016/s0006-3223(01)01239-2. [DOI] [PubMed] [Google Scholar]
  29. Leverich GS, Altshuler LL, Frye MA, Suppes T, Keck PE Jr., McElroy SL, Denicoff KD, Obrocea G, Nolen WA, Kupka R, Walden J, Grunze H, Perez S, Luckenbaugh DA and Post RM, 2003, Factors associated with suicide attempts in 648 patients with bipolar disorder in the Stanley Foundation Bipolar Network, J Clin Psychiatry, 64, 506–15, 10.4088/jcp.v64n0503. [DOI] [PubMed] [Google Scholar]
  30. Leverich GS, Post RM, Keck PE Jr, Altshuler LL, Frye MA, Kupka RW, Nolen WA, Suppes T, McElroy SL and Grunze H, 2007, The poor prognosis of childhood-onset bipolar disorder, The Journal of pediatrics, 150, 485–490, 10.1016/j.jpeds.2006.10.070. [DOI] [PubMed] [Google Scholar]
  31. Lewinsohn PM, Klein DN and Seeley JR, 1995, Bipolar disorders in a community sample of older adolescents: prevalence, phenomenology, comorbidity, and course, J Am Acad Child Adolesc Psychiatry, 34, 454–63, https://www.ncbi.nlm.nih.gov/pubmed/7751259. [PubMed] [Google Scholar]
  32. Linke J, King AV, Rietschel M, Strohmaier J, Hennerici M, Gass A, Meyer-Lindenberg A and Wessa M, 2012, Increased medial orbitofrontal and amygdala activation: evidence for a systems-level endophenotype of bipolar I disorder, Am J Psychiatry, 169, 316–25, 10.1176/appi.ajp.2011.11050711. [DOI] [PubMed] [Google Scholar]
  33. Long X, Wang X, Tian F, Cao Y, Xie H and Jia Z, 2022, Altered brain activation during reward anticipation in bipolar disorder, Translational Psychiatry, 12, 300, 10.1038/s41398-022-02075-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mair P and Wilcox R, 2020, Robust statistical methods in R using the WRS2 package, Behavior research methods, 52, 464–488, 10.3758/s13428-019-01246-w. [DOI] [PubMed] [Google Scholar]
  35. Nusslock R, Young CB and Damme KSF, 2014, Elevated reward-related neural activation as a unique biological marker of bipolar disorder: Assessment and treatment implications, Behaviour Research and Therapy, 62, 74–87, 10.1016/j.brat.2014.08.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Patino LR, Tallman MJ, Wen H, Adler CM, Welge JA and DelBello MP, 2023, Deficits in sustained attention in adolescents with bipolar disorder during their first manic episode, Journal of Affective Disorders, 339, 43–51, 10.1016/j.jad.2023.06.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pavuluri MN, Birmaher B and Naylor MW, 2005, Pediatric bipolar disorder: a review of the past 10 years, J Am Acad Child Adolesc Psychiatry, 44, 846–71, 10.1097/01.chi.0000170554.23422.c1. [DOI] [PubMed] [Google Scholar]
  38. Pavuluri MN, Henry DB, Devineni B, Carbray JA and Birmaher B, 2006, Child mania rating scale: development, reliability, and validity, Journal of the American Academy of Child & Adolescent Psychiatry, 45, 550–560, 10.1097/01.chi.0000205700.40700.50. [DOI] [PubMed] [Google Scholar]
  39. Perlis RH, Miyahara S, Marangell LB, Wisniewski SR, Ostacher M, DelBello MP, Bowden CL, Sachs GS and Nierenberg AA, 2004, Long-term implications of early onset in bipolar disorder: data from the first 1000 participants in the systematic treatment enhancement program for bipolar disorder (STEP-BD), Biol Psychiatry, 55, 875–81, 10.1016/j.biopsych.2004.01.022. [DOI] [PubMed] [Google Scholar]
  40. Redlich R, Dohm K, Grotegerd D, Opel N, Zwitserlood P, Heindel W, Arolt V, Kugel H and Dannlowski U, 2015, Reward Processing in Unipolar and Bipolar Depression: A Functional MRI Study, Neuropsychopharmacology, 40, 2623–2631, 10.1038/npp.2015.110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Rolls ET, Wan Z, Cheng W and Feng J, 2022, Risk-taking in humans and the medial orbitofrontal cortex reward system, NeuroImage, 249, 118893, 10.1016/j.neuroimage.2022.118893. [DOI] [PubMed] [Google Scholar]
  42. Russo SJ and Nestler EJ, 2013, The brain reward circuitry in mood disorders, Nature Reviews Neuroscience, 14, 609–625, 10.1038/nrn3381. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Satterthwaite TD, Kable JW, Vandekar L, Katchmar N, Bassett DS, Baldassano CF, Ruparel K, Elliott MA, Sheline YI and Gur RC, 2015, Common and dissociable dysfunction of the reward system in bipolar and unipolar depression, Neuropsychopharmacology, 40, 2258–2268, 10.1038/npp.2015.75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Schreiter S, Spengler S, Willert A, Mohnke S, Herold D, Erk S, Romanczuk-Seiferth N, Quinlivan E, Hindi-Attar C, Banzhaf C, Wackerhagen C, Romund L, Garbusow M, Stamm T, Heinz A, Walter H and Bermpohl F, 2016, Neural alterations of fronto-striatal circuitry during reward anticipation in euthymic bipolar disorder, Psychological Medicine, 46, 3187–3198, 10.1017/S0033291716001963. [DOI] [PubMed] [Google Scholar]
  45. Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF and Krystal AD, 2024, Closed-loop neurostimulation for the treatment of psychiatric disorders, Neuropsychopharmacology, 49, 163–178, 10.1038/s41386-023-01631-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Sharma A, Satterthwaite TD, Vandekar L, Katchmar N, Daldal A, Ruparel K, Elliott MA, Baldassano C, Thase ME, Gur RE, Kable JW and Wolf DH, 2016, Divergent relationship of depression severity to social reward responses among patients with bipolar versus unipolar depression, Psychiatry Research: Neuroimaging, 254, 18–25, 10.1016/j.pscychresns.2016.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Simonetti A, Saxena K, Koukopoulos AE, Janiri D, Lijffijt M, Swann AC, Kotzalidis GD and Sani G, 2022, Amygdala structure and function in paediatric bipolar disorder and high-risk youth: A systematic review of magnetic resonance imaging findings, The World Journal of Biological Psychiatry, 23, 103–126, 10.1080/15622975.2021.1935317. [DOI] [PubMed] [Google Scholar]
  48. Singh MK, Chang KD, Kelley RG, Cui X, Sherdell L, Howe ME, Gotlib IH and Reiss AL, 2013, Reward processing in adolescents with bipolar I disorder, Journal of the American Academy of Child & Adolescent Psychiatry, 52, 68–83, 10.1016/j.jaac.2012.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Trost S, Diekhof EK, Zvonik K, Lewandowski M, Usher J, Keil M, Zilles D, Falkai P, Dechent P and Gruber O, 2014, Disturbed anterior prefrontal control of the mesolimbic reward system and increased impulsivity in bipolar disorder, Neuropsychopharmacology, 39, 1914–1923, 10.1038/npp.2014.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Wakatsuki Y, Ogura Y, Hashimoto N, Toyomaki A, Miyamoto T, Nakagawa S, Inoue T and Kusumi I, 2022, Subjects with bipolar disorder showed different reward system activation than subjects with major depressive disorder in the monetary incentive delay task, Psychiatry and Clinical Neurosciences, 76, 393–400, 10.1111/pcn.13429. [DOI] [PubMed] [Google Scholar]
  51. Walton ME, Behrens TE, Noonan MP and Rushworth MF, 2011, Giving credit where credit is due: orbitofrontal cortex and valuation in an uncertain world, Annals of the New York Academy of Sciences, 1239, 14–24, 10.1111/j.1749-6632.2011.06257.x. [DOI] [PubMed] [Google Scholar]
  52. Wechsler D, 1999, Wechsler Abbreviated Scale of Intelligence,; Wood A, Kroll L, Moore A and Harrington R, 1995, Properties of the mood and feelings questionnaire in adolescent psychiatric outpatients: a research note, Journal of child psychology and psychiatry, 36, 327–334, 10.1111/j.1469-7610.1995.tb01828.x. [DOI] [PubMed] [Google Scholar]
  53. Yip SW, Worhunsky PD, Rogers RD and Goodwin GM, 2015, Hypoactivation of the ventral and dorsal striatum during reward and loss anticipation in antipsychotic and mood stabilizer-naive bipolar disorder, Neuropsychopharmacology, 40, 658–666, 10.1038/npp.2014.215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Young KD, Zotev V, Phillips R, Misaki M, Yuan H, Drevets WC and Bodurka J, 2014, Real-time FMRI neurofeedback training of amygdala activity in patients with major depressive disorder, PloS one, 9, e88785, 10.1371/journal.pone.0088785. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1963709-supplement-1.docx (874.8KB, docx)

RESOURCES