Abstract
Differentiating bipolar disorder from unipolar depression is one of the most difficult clinical questions posed in pediatric psychiatric practices, as misdiagnosis can lead to severe repercussions for the affected child. This study aimed to examine the existing literature that investigates brain differences between bipolar and unipolar mood disorders in children directly, across all neuroimaging modalities. We performed a systematic literature search through PubMed, PsycINFO, Embase, and Medline databases with defined inclusion and exclusion criteria. Nine research studies were included in the systematic qualitative review, including three structural MRI studies, five functional MRI studies, and one MR spectroscopy study. Relevant variables were extracted and brain differences between bipolar and unipolar mood disorders in children as well as healthy controls were qualitatively analyzed. Across the nine studies, our review included 228 subjects diagnosed with bipolar disorder, 268 diagnosed with major depressive disorder, and 299 healthy controls. Six of the reviewed studies differentiated between bipolar and unipolar mood disorders. Differentiation was most commonly found in the anterior cingulate cortex (ACC), insula, and dorsal striatum (putamen and caudate) brain areas. Despite its importance, the current neuroimaging literature on this topic is scarce and presents minimal generalizability.
Keywords: Bipolar disorder, Unipolar depression, Pediatrics, Anterior cingulate cortex, Insula, Dorsal striatum
1. Introduction
Differentiating bipolar disorder from unipolar depression in children presents a unique clinical challenge. The two mood disorders respond differently to treatment, and misdiagnosis can have very serious consequences for the affected child. It is estimated that at least 60% of patients diagnosed with bipolar disorder are misdiagnosed with unipolar depression, or major depressive disorder (MDD), and subsequently treated with antidepressant medication (Hirschfeld et al., 2003; Goodwin and Redfield Jamison 2007). While antidepressants are considered the first-line pharmacological treatment for MDD, the use of antidepressants without adequate mood stabilization in bipolar disorder could further exacerbate mood and induce manic symptoms, psychomotor agitation, mixed states, and increased risk of suicidal behaviors (Faedda et al., 2004; Akiskal and Benazzi 2005; Baldessarini et al., 2005). This prognosis emphasizes the need for research aimed to differentiate these affective conditions (Cardoso de Almeida and Phillips 2013).
The clinical characteristics of bipolar and unipolar disorders in youth have been investigated in an effort to identify meaningful differences between the mood disorders. Uchida et al. (2015) conducted a systematic literature review of such studies, finding four pediatric studies that examined differences between the two disorders. The review determined that youth with bipolar disorder had higher levels of depression severity, psychiatric comorbidity, associated impairment, and familial history of psychiatric illness in first-degree relatives compared to children with unipolar depression (Wozniak et al., 2004; Luby and Belden 2008; Merikangas et al., 2012; Shon et al., 2014). However, whether these putative clinical differences also have different neural underpinning remains unclear.
Differentiating bipolar disorder and unipolar depression with an objective measure is an area of high clinical and scientific significance. Objective measures play a crucial role in guiding clinician treatment choices as well as aid the development of novel therapeutic approaches for children, both of which can optimize clinical and functional outcomes for all depressed patients (Cardoso de Almeida and Phillips 2013).
Numerous neuroimaging studies have sought to determine the neural substrates distinguishing bipolar disorder and unipolar depression by studying adult subjects. Han et al. (2019) conducted a literature review that summarized the current functional and structural MRI literature, which suggests functional and structural alterations in emotion- or reward-processing neural circuits between bipolar and unipolar depression with different activation patterns in neural networks of the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum. Han et al. (2019) also included four neuroimaging studies distinguishing bipolar from unipolar depression in adolescents in their review (MacMaster et al., 2008; Diler et al., 2013; Diler et al., 2014; MacMaster et al., 2014). The researchers noted that these studies yielded findings both similar and contradictory to those in adults, and therefore age might be a confounding factor affecting the results. Still, further investigation of this topic in children is needed.
Few studies have attempted to identify neural biomarkers that objectively differentiate bipolar and unipolar mood disorders in children. Serafini et al. (2014) conducted a systematic literature review of 17 pediatric neuroimaging studies examining white and gray matter integrity in bipolar and unipolar mood disorders detected by diffusion tensor imaging (DTI) or voxel-based analysis (VBM). Overall, greater white matter abnormalities were found in children with bipolar disorder compared to children with unipolar depression. However, the studies included in this review did not directly compare groups of children diagnosed with bipolar versus unipolar disorder, nor did it include other imaging methodologies.
To address this gap in the literature, the main aim of the present study was to re-examine the neuroimaging literature that investigates the differences between bipolar and unipolar mood disorders in children directly, across all neuroimaging modalities. To this end, we conducted a systematic review of the extant literature to summarize the knowledge on this important issue. To the best of our knowledge, this is the first investigation of this topic.
2. Methods
2.1. Database search
We performed a systematic literature search through PubMed, PsycINFO, Embase, and Medline databases utilizing the following search algorithm: (youth or pediatric or children or adolescents) AND (imaging or MRI) AND (manic-depressed or manic depression or bipolar) AND (depression or MDD or unipolar). Reference lists of retrieved papers were further screened, and papers that possibly met our inclusion criteria were reviewed and assessed for inclusion.
2.2. Selection criteria
We implemented the following inclusion criteria: (1) original research in a peer-reviewed journal, (2) sample includes children (3) primary focus is neuroimaging (4) and directly compares bipolar disorder versus major depressive disorder. The following exclusion criteria were also applied: (1) sample includes youth at-risk for mood disorders, (2) study focus is on other psychiatric, medical or neurological disorders, (3) sample includes adults only (4) clinical trials, (5) literature reviews and meta-analyses, (6) and animal research. Research studies that were not available in full text or written in English, book chapters, editorials, conference abstracts, and case studies were not reviewed. The senior author (JB) and the lead author (CK) screened the articles for relevance by examining the abstracts to identify relevant articles in full text and assess their eligibility.
2.3. Data extraction
We extracted the following variables from relevant studies: imaging modality, sample age range and size, diagnostic tools used, comorbidities, medication history at the time of scan, and image analyses.
2.4. Qualitative analysis
In addition to extracting relevant variables from included articles, we also qualitatively analyzed the results, with a particular focus on the brain area differences between bipolar and unipolar mood disorders as well as healthy controls. We also examined the effects of psychotropic medication and the potential impact it may have had on the imaging results.
3. Results
3.1. Number of selected studies
Fig. 1 outlines our process for screening and identifying eligible articles using the algorithm described above. From the four database searches, a total of 1151 records were identified and screened by two of the authors (JB and CK). After further review, 405 records were identified as duplicates and subsequently removed. After obtaining and assessing 746 full text research studies, 737 research studies were excluded (see Fig. 1 for the reasons of exclusion). Nine research studies met the aforementioned inclusion and exclusion criteria, and therefore were included in the systematic qualitative review.
Fig. 1.
Flow Diagram of Study Selection Process, as Outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement.
3.2. Types of studies selected
As shown in Table 1, our review included three structural magnetic resonance imaging (sMRI) studies, five functional magnetic resonance imaging (fMRI) studies (three task-based and two resting-state), and one magnetic resonance spectroscopy (MRS) study. All included studies utilized a cross-sectional design. Out of the nine studies, seven studies implemented a controlled design (MacMaster et al., 2008; Diler et al., 2013; Diler et al., 2014; MacMaster et al., 2014; Jiang et al., 2017; Yao et al., 2018; Tannous et al., 2018), while the remaining two studies did not (Ford et al., 2013; Shi et al., 2014).
Table 1.
Summary of studies included in the qualitative review.
| Author (Year) | Age Range | N = | Methods | Image Acquisition | BD vs UD Findings | BD and UD vs HC Findings | Medication Effects |
|---|---|---|---|---|---|---|---|
| Structural Magnetic Resonance Imaging (sMRI) | |||||||
| MacMaster et al. (2008) | 14 to 20 | 10 BD 10 UD 10 HC |
|
|
No significant differences. |
|
Medication was not controlled for in the analysis. |
| MacMaster et al. (2014) | 11 to 21 | 14 BD 32 UD |
|
|
BD subjects had greater left and right ACC white matter volumes compared to UD subjects. |
|
Medication was not controlled for in the analysis. |
| Tannous et al. (2018) | 8 to 18 | 57 BD 30 UD 54 HC |
|
|
No significant differences. |
|
Medication was not controlled for in the analysis. |
| Resting-State Functional Magnetic Resonance Imaging (fMRI) | |||||||
| Ford et al. (2013) | Not reported (mean age= 20) | 15 BD 15 UD |
|
|
|
N/A | No significant differences were found between BD subjects medicated with lithium and those who were not. |
| Jiang et al. (2017) | 13 to 30 | 46 BD 57 UD 80 HC |
|
|
|
|
|
| Yao et al. (2018) | 13 to 45 | 55 BD 76 UD 113 HC |
|
|
|
|
No significant differences were found between medicated and un-medicated subjects. |
| Task-Based Functional Magnetic Resonance Imaging (fMRI) | |||||||
| Diler et al. (2013) | 12 to 17 | 10 BD 10 UD 10 HC |
|
|
|
|
No significant differences were found between medicated and un-medicated subjects. |
| Diler et al. (2014) | 12 to 17 | 12 BD 10 UD 10 HC |
|
|
No significant differences. |
|
No significant differences between medicated and un-medicated subjects. |
| Magnetic Resonance Spectroscopy (MRS) | |||||||
| Shi et al. (2014) | 13 to 20 | 9 BD 28 UD |
|
|
|
N/A | Medication was not controlled for in the analysis. |
Abbreviation key: Bipolar disorder (BD); Unipolar depression (UD); Healthy control (HC); Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version or Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL); Structured Clinical Interview for Diagnosis-IV (SCID); Anterior cingulate cortex (ACC); Amplitude of low-frequency fluctuations (ALFF); Proton-1 MRS (1H-MRS); Choline-containing compounds (tCho); Creatine resonance (Cre).
3.3. Participants
Across all nine studies, our review included 228 subjects diagnosed with bipolar disorder, 268 diagnosed with major depressive disorder, and 299 healthy controls. All studies that recruited subjects under the age of 18 years old utilized the Kiddie-Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL) to confirm diagnosis of bipolar disorder or major depressive disorder (Kaufman et al., 1997). Studies with subjects over the age of 18 years old utilized the Structured Clinical Interview for Diagnosis (SCID) (First et al., 1997). The diagnostic groups were based on DSM-IV criteria. For all controlled studies, recruited healthy controls did not have a familial or personal history of psychiatric illness.
The studies that included detailed recruitment summaries included children with bipolar disorder and major depression from psychiatric clinics and healthy controls from the community. Medicated subjects were most commonly receiving mono or combined therapy of the following medication classes at the time of the scan: mood stabilizers, atypical antipsychotics, anxiolytics, and antidepressants. Four out of nine studies controlled for medication effects, but only one study found meaningful differences between medicated and medication naïve subjects in the bipolar disorder group (Jiang et al., 2017).
3.4. Structural MRI studies
One sMRI study found a significant brain difference between children with bipolar and unipolar mood disorders. When examining regional brain morphology, MacMaster et al. (2014) found significant volumetric brain differences between bipolar and unipolar subjects. Children with bipolar disorder had increased ACC white matter volumes in both the right and left hemisphere compared to children with unipolar depression.
All three sMRI studies found significant differences when mood disorder results were compared to healthy controls. MacMaster et al. (2008) found that children with bipolar and unipolar disorder had greater pituitary gland volumes compared to healthy controls. MacMaster et al. (2014) found that both pediatric bipolar and unipolar subjects had reduced left hemisphere hippocampal volumes compared to healthy controls. Additionally, only bipolar subjects had reduced left and right hemisphere putamen volumes and only unipolar subjects had reduced right ACC white and gray matter volumes when compared to healthy controls. Lastly, Tannous et al. (2018) found that children with bipolar disorder had reduced hippocampal subfield volumes compared to healthy controls. Although two studies reported reduced hippocampal volumes between bipolar depression and healthy controls (MacMaster et al., 2014; Tannous et al., 2018), no common structural brain differences were identified to distinguish bipolar from unipolar disorder subjects.
3.5. Functional MRI studies
Three resting-state fMRI studies found significant differences between bipolar and unipolar disorder (Ford et al., 2013; Jiang et al., 2017; Yao et al., 2018). Using whole brain resting-state fMRI, Ford et al. (2013) examined Default Mode Network (DMN) connectivity in correlation to scores on the Bipolarity Index (BI). Activation in the putamen, caudate, and insula positively correlated with BI, and as expected, all bipolar subjects had higher BI scores compared to unipolar subjects. In contrast, activation in the postcentral gyrus and posterior cingulate gyrus negatively correlated with BI scores. Jiang et al. (2017) used amplitude low-frequency fluctuations (ALFF) to compare neural activation changes. They found that when comparing youth with bipolar disorder and unipolar disorder, youth with bipolar disorder had increased ALFF in the right cortico-limbic neural system (including the right hemisphere parahippocampal gyrus, hippocampus, amygdala, temporal pole, fusiform, inferior temporal gyrus, superior frontal gyrus and orbitofrontal cortex). Youth with bipolar disorder also had reduced bilateral calcarine fissure and lingual gyrus ALFF activity compared to youth with unipolar disorder. Whole brain voxel-wise Regional Homogeneity (ReHo) was used to identify abnormal brain activity in Yao et al. (2018). ReHo is used to analyze resting-state fMRI data, by measuring the similarity of the time series of a given voxel to those of its nearest neighbors in a voxel-wise way (Zang et al., 2004). They found that youth with bipolar disorder had greater left hemisphere orbital inferior frontal gyrus, middle frontal gyrus, and inferior frontal gyrus ReHo values, but reduced left insula and superior temporal gyrus ReHo values when compared to youth with unipolar disorder.
Two resting-state fMRI studies found significant differences when mood disorder results were compared to healthy controls. Jiang et al. (2017) reported that youth with bipolar disorder and unipolar disorder had greater left hemisphere hippocampal gyrus, hippocampus, amygdala, temporal pole, caudate, putamen, and ventral anterior cingulate ALFF activity when compared to healthy controls. Further, subjects with bipolar disorder also had greater right hemisphere superior frontal gyrus and orbitofrontal cortex ALFF activity compared to healthy controls. Yao et al. (2018) reported that youth with bipolar disorder and unipolar disorder had greater ReHo values in the left hemisphere orbital inferior frontal gyrus, middle frontal gyrus, and inferior frontal gyrus activity, and reduced ReHo values in the left hemisphere insula and superior temporal gyrus activity when compared to healthy controls.
One task-based fMRI study found significant differences in functional neural activity between bipolar and unipolar disorder. Diler et al. (2013) identified differential patterns of functional neural activity during emotional processing to intense, mild, and neutral happy or fearful faces. They found that children with bipolar disorder had reduced right hemisphere insula and middle temporal cortical activity compared to children with unipolar disorder when exposed to intense happy faces. Additionally, children with bipolar disorder had reduced right precentral cortical activity compared to children with unipolar disorder when exposed to intense fearful faces.
While only one task-based fMRI study found differences between bipolar and unipolar disorder, both task-based fMRI studies found significant differences when bipolar and unipolar subject results were compared to healthy controls. Diler et al. (2013) found that both children with bipolar and unipolar disorder had reduced left right parahippocampal activity to mild happy faces compared to healthy controls. While just children with bipolar disorder had greater left hemisphere ventrolateral PFC, insula, and postcentral cortical activity to mild fearful faces compared to healthy controls, and just children with unipolar disorder had greater left hemisphere postcentral cortical, right superior temporal, and right occipital cortical activity compared to healthy controls. Diler et al. (2014) found that both children with bipolar and unipolar disorder had greater left hemisphere superior temporal gyrus and caudate activity, while just children with bipolar disorder also had greater left hemisphere ACC activity, when compared to healthy controls.
Across the five fMRI studies, two studies identified reduced insula activity (Diler et al., 2013; Yao et al., 2018) between youth with bipolar and unipolar disorder. Only one study did not find significant differences between the two mood disorders, but concurrently found significant differences between children with mood disorders and healthy controls (Diler et al., 2014). Two studies found increased caudate activity (Diler et al., 2014; Jiang et al., 2017), however only one study had statistical significance, and three studies found differences in superior temporal gyrus activity (Diler et al., 2013; Diler et al., 2014; Yao et al., 2018) when compared to healthy controls.
3.6. MR spectroscopy study
In the only spectroscopy study identified, Shi et al. (2014) found that children with bipolar depression had a significantly lower ACC tCho/Cre ratio compared to children with unipolar disorder. The study did not identify other significant differences in other 1H-MRS metabolites.
4. Discussion
Our systematic review of the extant literature examined studies that evaluated brain differences between youth with bipolar and unipolar mood disorders via neuroimaging. Six pediatric neuroimaging studies documented that brain structure, function, connectivity and chemistry in the cortico-limbic-striatal neural system differentiated children with bipolar disorder and unipolar depression. Albeit limited, these results suggest that neuroimaging could play a role in differentiating pediatric bipolar from unipolar mood disorders.
Two of the reviewed papers showed differences in the ACC between bipolar and unipolar children. MacMaster et al. (2014) reported that children with bipolar disorder had increased right and left ACC white matter volumes compared to those with unipolar disorder. They also noted a non-significant trend toward smaller ACC gray matter volume in children with unipolar depression compared to those with bipolar disorder. Using spectroscopy, Shi et al. (2014) found that children with bipolar disorder had increased ACC choline/creatine ratios compared to subjects with unipolar disorder. Even though the two studies used different imaging modalities to find their results, abnormality in brain volume and chemical composition of brain tissue in the ACC may be used to establish differential diagnoses between the bipolar and unipolar mood disorders. Consistencies were found with the adult literature. Similar to MacMaster et al. (2014), both Redlich et al. (2014) and Chen et al. (2018) found that adults with bipolar disorder had increased gray matter volume in the ACC compared to those with unipolar disorder. However, in contrast with Shi et al. (2014), Zhong et al. (2014) did not find a significant difference in ACC choline/creatine ratios between adults with bipolar and unipolar disorder. In addition to structural and metabolic studies, functional studies have also reported increased ACC activity in adults with bipolar disorder compared to adults with unipolar disorder (Delvecchio et al., 2012; Burger et al., 2017). Taken together, these findings support the ACC, which plays a key role in emotion assessment, learning, and regulation, as a region of interest in the cortico-limbic circuit to differentiate between bipolar and unipolar mood disorders.
Another potential important region of interest is the insula, as three of the reviewed papers showed differences in insular activity between bipolar and unipolar children. Ford et al. (2013) reported that the severity of pediatric bipolar symptomatology correlated positively with increased resting-state insular activation, while Yao et al. (2018) reported that bipolar youth had reduced insular ReHo value at resting-state. Similar to Yao et al. (2018), Diler et al. (2013) reported that bipolar youth had reduced insular activity in response to happy face stimuli during an emotion-based task. Although Ford et al. (2013) and Yao et al. (2018) used resting-state fMRI and Diler et al. (2013) used task-based fMRI, their findings collectively suggest that the insula is an important region of interest for future studies assessing for brain differences in the two mood disorders. Reduced insular activity in bipolar youth is consistent with other pediatric studies such as Bebko et al. (2015), which reported that the greater the severity of emotional dysregulation, the greater reductions of insular activity were found in children with depression. Severe dysregulation is a core feature of bipolar presentation in children (Biederman et al., 2013; Uchida et al., 2014), and therefore we expect greater insular activity reductions in bipolar children compared to unipolar children. The insular cortex has also been investigated in adult mood disorder neuroimaging research. For instance, Liu et al. (2012) reported that adults with bipolar disorder had greater reductions in insular ALFF activity compared to those with unipolar depression. Collectively, these findings support the insula, which plays a major role in processing both emotional recognition and regulation, as a region of interest within the cortico-limbic-striatal system to differentiate between bipolar and unipolar mood disorders.
A third target of neural biomarker research has been the dorsal striatum (putamen and caudate), a brain area that contributes directly to decision-making. Using resting-state fMRI, Ford et al. (2013) reported that the severity of bipolar symptomatology correlated positively with increased resting-state putamen activation. While using task-based fMRI, Diler et al. (2014) found that longer reaction time was negatively correlated with left caudate activity in pediatric subjects with unipolar depression only. Although the available adult literature lacks fMRI evidence that the dorsal striatum can be used as a differentiating neural biomarker of bipolar and unipolar mood disorders, one adult study found significantly increased dorsal striatum volume in adults with bipolar disorder versus healthy controls (Strakowski et al., 2002). This finding is consistent with the pediatric literature comparing children with bipolar disorder and healthy controls (DelBello et al., 2004). Additionally, in a study examining adults with bipolar disorder, Brambilla et al. (2001) found that the duration of bipolar illness may be related to putamen volume. Considering the dorsal striatum’s role in decision-making through the integration of emotional, cognitive, and sensorimotor information, more research is needed to further examine whether the dorsal striatum can be used to differentiate between pediatric bipolar and unipolar disorders.
In addition to ACC, insula, and dorsal striatum, other brain areas were found to differentiate between bipolar and unipolar mood disorders including the limbic regions (parahippocampal gyrus, hippocampus, amygdala, and posterior cingulate), temporal region (superior, middle, and inferior temporal gyri, fusiform gyrus, and temporal pole), frontal region (superior, middle, and inferior frontal gyri, orbitofrontal cortex, and precentral cortex), occipital region (calcarine fissure and lingual gyrus), and parietal region (postcentral gyrus) (Diler et al., 2013; Ford et al., 2013; Jiang et al., 2017; Yao et al., 2018).
Many of these brain areas play a significant role in neural systems that support emotion processing and regulation, cognition, and reward processing (Cardoso de Almeida and Phillips 2013; Han et al., 2019). Taken together, these brain-based differences indicate that further investigation of dysfunction in the cortico-limbic-striatal neural system could be useful in understanding the underlying mechanisms of these two disorders.
This review is not without limitations. First, there were a scant number of research studies available on this topic. This contributed to the review and comparison of results from research studies that differed in methods, including image acquisition and analytical approach. Further, the studies identified had relatively small sample sizes with limited information available on subject demographics such as race and socioeconomic status. Our review was also limited to research studies published in the English language, which may have biased conclusions. Additionally, many of the research studies did not control for important confounders such as mood state, pharmacotherapy effects, and psychiatric comorbidities. One approach to mitigate these problems is to target children at risk for mood disorders, and thereby avoiding the confounders of chronicity and treatment effects. Despite these limitations, our review identified potential regions of interest that could be targeted in future neuroimaging studies aimed at the differentiation of bipolar and unipolar mood disorders.
5. Funding
This research was indirectly supported by the MGH Pediatric Psychopharmacology Council Fund. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of Competing Interest
Dr. Joseph Biederman is currently receiving research support from the following sources: AACAP, Feinstein Institute for Medical Research, Food & Drug Administration, Genentech, Headspace Inc., NIDA, Pfizer Pharmaceuticals, Roche TCRC Inc., Shire Pharmaceuticals Inc., Sunovion Pharmaceuticals Inc., Tris, and NIH. Dr. Biederman’s program has received departmental royalties from a copyrighted rating scale used for ADHD diagnoses, paid by Bracket Global, Ingenix, Prophase, Shire, Sunovion, and Theravance; these royalties were paid to the Department of Psychiatry at MGH. In 2020: Through MGH corporate licensing, Dr. Biederman has a US Patent (#14/027,676) for a non-stimulant treatment for ADHD, a US Patent (#10,245,271 B2) on a treatment of impaired cognitive flexibility, and a patent pending (#61/233,686) on a method to prevent stimulant abuse. He receives honoraria from the MGH Psychiatry Academy for tuition-funded CME courses. In 2019, Dr. Biederman was a consultant for Akili, Avekshan, Jazz Pharma, and Shire/Takeda. He received research support from Lundbeck AS and Neuro-centria Inc. Through MGH CTNI, he participated in a scientific advisory board for Supernus. He received honoraria from the MGH Psychiatry Academy for tuition-funded CME courses. In 2018, Dr. Biederman was a consultant for Akili and Shire. He received honoraria from the MGH Psychiatry Academy for tuition-funded CME courses. Ms. Caroline Kelberman, Dr. Vincenza Spera, Dr. Marco Maiello, Ms. Allison Green, and Dr. Mai Uchida have no conflicts of interest to declare.
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