Affective Processing in Pediatric Bipolar Disorder and Offspring of Bipolar Parents

Neurocognitive Correlates of Cerebellar Volumetric Alterations in Youth with Pediatric Bipolar Spectrum Disorders and Bipolar Offspring

BACKGROUND

Emerging evidence points towards the involvement of the cerebellum in the processing of emotions and pathophysiology of mood disorders. However, cerebellar and related cognitive alterations in youth with pediatric bipolar disorder (PBD) and those at high risk to develop the disorder, such as bipolar offspring (BD-OFF) are not clearly defined.

OBJECTIVE

To investigate cerebellar gray and white matter volumes, cognition, and their relationship in youth with PBD and BD-OFF.

METHODS

Thirty youth (7 to 17 years, inclusive) with PBD, 30 BD-OFF and 40 healthy controls (HC) were recruited. Study participants underwent a computer-based cognitive battery assessing affective processing, executive function, attention, psychomotor speed, and learning. Three-tesla MRI scan was performed to assess cerebellar white and gray matter volumes. Cerebellar segmentation was performed with FreeSurfer. Statistical analyses include between-group differences in cognitive domains, cerebellar gray, and white matter volumes. Relationships between cerebellar volumes and cognitive domains were examined.

RESULTS

Youth with PBD showed greater cerebellar gray matter volumes than both BD-OFF and HC, whereas no differences were present between BD-OFF and HC. Both youth with PBD and BD-OFF showed altered processing of negative emotions and a bias towards positive emotions. In youth with PBD and BD-OFF, greater impairment in the processing of emotions correlated with greater cerebellar gray matter volumes.

CONCLUSION

The present findings corroborate hypotheses on cerebellar involvement in the processing of emotions and the pathophysiology of PBD. The presence of cerebellar dysfunction in BD-OFF is unclear.

Age moderates the relationship between affective response inhibition and bipolar disorder in adults

BACKGROUND

Patients with bipolar disorder (BD) often have impairments in neurocognition, including affective processing and affective response inhibition. While studies suggest that cognitive control in general may decline with age in BD, less is known about age-related changes in response inhibition to emotionally salient information.

METHODS

258 participants with BD and 54 healthy controls, ages 18-70, completed the Cambridge Neuropsychological Test Automated Battery (CANTAB) Affective Go/No-Go task to assess affective response inhibition to positive and negative valenced stimuli. We examined the relationship between BD and affective response inhibition (number of commission and omission errors and reaction time), as well as a potential moderating effect of age, using mixed effects linear regression models.

RESULTS

The BD group made more omission and commission errors overall than the control group (p < 0.018). We observed a significant 3-way group-by-age-by-valence interaction for reaction time (p = 0.006). Within BD, a slower reaction time to negative than positive stimuli was found in middle and older age groups (p < 0.012), but not in the younger age group. No significant moderating effect of age was observed within the control group.

CONCLUSIONS

These cross-sectional findings indicate that compared with healthy controls, individuals with BD display differential and age-related effects in inhibition to emotionally salient information that is valence-dependent. The observed pattern of a switch in bias from negative to positive stimuli with age in BD may aid in our understanding of the progression of neurocognitive changes with aging in BD, as well as inform targeted treatments for cognitive symptoms.

Cognitive correlates of impulsive aggression in youth with pediatric bipolar disorder and bipolar offspring

BACKGROUND

Youth with bipolar disorder (BD) and offspring of individuals with BD (BD-OFF) are characterized by higher levels of impulsive and overt aggression. The cognitive basis underlying these aggressive behaviors are not clarified in this population. The aim of this study was to investigate the relationship between cognitive alterations and aggressive behavior in youth with BD and BD-OFF.

METHODS

Forty-two youth with BD, 17 BD-OFF and 57 healthy controls (HCs) were administered the Modified Overt Aggression Scale (MOAS), the Cambridge Neuropsychological Test Automated Battery (CANTAB), the Young Mania Rating Scale (YMRS) and the Children's Depression Rating Scale (CDRS). Multiple linear regression analyses were performed in the three groups separately. In each group, tests scores from the CANTAB were predictors. MOAS subscale scores and MOAS total scores were dependent variables. Results are corrected for age, IQ and mood state.

RESULTS

Both youth with BD and BD-OFF showed positive correlations between impairment in executive functions and levels of verbal aggression. In youth with BD, altered processing of either positive and negative stimuli positively correlated with MOAS total scores, whereas in BD-OFF, such relationship was negative.

CONCLUSIONS

Impulsive aggressive behaviors in youth with BD arise from a combination of altered affective processing and executive dysfunction. The negative relationship between affective processing and aggression in BD-OFF suggested the presence of possible mechanisms of resilience in this population.

Longitudinal course of depressive symptom severity among youths with bipolar disorders: Moderating influences of sustained attention and history of child maltreatment

BACKGROUND

Pediatric bipolar disorders are often characterized by disruptions in cognitive functioning, and exposure to child maltreatment (e.g., physical and sexual abuse) is associated with a significantly poorer course of illness. Although clinical and developmental research has shown maltreatment to be robustly associated with poorer cognitive functioning, it is unclear whether maltreatment and cognitive function jointly influence the clinical course of bipolar symptoms.

METHODS

This secondary analysis examined moderating effects of lifetime childhood physical and sexual abuse, and cognitive disruptions (sustained attention, affective information processing), on longitudinal ratings of depression symptom severity in youths from the Course and Outcome of Bipolar Youth (COBY) study, examined from intake (M = 12.24 years) through age 22 (N = 198; 43.9% female; Mean age of bipolar onset = 8.85 years).

RESULTS

A significant moderating effect was detected for sustained attention and maltreatment history. In the context of lower sustained attention, maltreatment exposure was associated with higher depression symptom severity during childhood, but not late adolescence. There was no association between maltreatment and symptom severity in the context of higher sustained attention, and no association between attention and depression symptom severity for non-maltreated youths.

LIMITATIONS

Depression symptom ratings at each assessment were subject to retrospective recall bias despite the longitudinal design. Cognitive assessments were administered at different ages across youths.

CONCLUSIONS

Depressive symptoms in pediatric bipolar may be jointly moderated by impairments in attention and exposure to maltreatment. Assessment of these risks, particularly in childhood, may be beneficial for considering risk of recurrence or chronicity of depressive symptoms.

The impact of familial risk and early life adversity on emotion and reward processing networks in youth at-risk for bipolar disorder

A recently developed risk calculator for bipolar disorder (BD) accounts for clinical and parental psychopathology. Yet, it is understood that both familial predisposition and early life adversity contribute to the development of BD. How the interplay between these two factors influence emotion and reward processing networks in youth at risk for BD remains unclear. In this exploratory analysis, offspring of BD parents performed emotion and reward processing tasks while undergoing a fMRI scan. Risk calculator score was used to assess risk for developing BD in the next 5 years. Environmental risk was tabulated using the Stressful Life Events Schedule (SLES). Emotion and reward processing networks were investigated for genetic and/or environment interactions. Interaction effects were found between risk calculator scores, negative SLES score and activity in right amygdala and bilateral fusiform gyri during the emotion processing task, as well as activity in the fronto-, striatal, and parietal regions during the reward processing task. Our findings are preliminary; however, they support the unique and interactive contributions of both familial and environmental risk factors on emotion and reward processing within OBP. They also identify potential neural targets to guide development of interventions for youth at greatest risk for psychiatric disorders.

Pediatric Bipolar Disorder: Onset, Risk Factors, and Protective Factors

The current article discusses the diagnosis of bipolar disorder in children throughout the years as it has evolved, focusing on very early-onset and early-onset bipolar disorder. Proper care of children with bipolar disorder requires a thorough understanding of the subtleties in symptoms at different developmental ages, as well as a shift in diagnostic thinking, which grew to include disruptive mood dysregulation disorder (DMDD). DMDD was added to address potential overdiagnosis of an already unusual diagnosis in young children. Critical discussion of risk factors, protective factors, and lack of data to support protective factors in the literature follows. Implications for advanced practice RNs are included, as these children transition from pediatric practice to adult practice. [Journal of Psychosocial Nursing and Mental Health Services, 57(9), 32-37.].

The use of component-wise gradient boosting to assess the possible role of cognitive measures as markers of vulnerability to pediatric bipolar disorder

BACKGROUND AND AIMS

Cognitive impairments are primary hallmarks symptoms of bipolar disorder (BD). Whether these deficits are markers of vulnerability or symptoms of the disease is still unclear. This study used a component-wise gradient (CGB) machine learning algorithm to identify cognitive measures that could accurately differentiate pediatric BD, unaffected offspring of BD parents, and healthy controls.

METHODS

59 healthy controls (HC; 11.19 ± 3.15 yo; 30 girls), 119 children and adolescents with BD (13.31 ± 3.02 yo, 52 girls) and 49 unaffected offspring of BD parents (UO; 9.36 ± 3.18 yo; 22 girls) completed the CANTAB cognitive battery.

RESULTS

CGB achieved accuracy of 73.2% and an AUROC of 0.785 in classifying individuals as either BD or non-BD on a dataset held out for validation for testing. The strongest cognitive predictors of BD were measures of processing speed and affective processing. Measures of cognition did not differentiate between UO and HC.

CONCLUSIONS

Alterations in processing speed and affective processing are markers of BD in pediatric populations. Longitudinal studies should determine whether UO with a cognitive profile similar to that of HC are at less or equal risk for mood disorders. Future studies should include relevant measures for BD such as verbal memory and genetic risk scores.

Neuropsychological Functioning and Temperament Traits in a Czech Sample of Children and Adolescents at Familial Risk of Bipolar Disorder

Background: Although a positive family history is the strongest predictor for bipolar disorder (BD), most offspring of BD parents (BO) will not develop the disorder. Identification of vulnerability markers for BD is essential for specific individual risk estimation. Impairments in cognitive functioning and the presence of specific temperament traits are considered promising candidates. Methods: Sixty-three BO (48% female; 11.8 ± 3.3 years) and 54 control offspring (CO; 44% female; 12.3 ± 3.2 years) comparable in sex (p = 0.4) and age (p = 0.4) were enrolled. Detection of current sub/threshold mood symptoms by the Kiddie Schedule for Affective Disorders and Schizophrenia and General Behavior Inventory was applied to separate BO into ultrahigh-risk (UHR) and high-risk (HR) subgroups. Cognitive functions were tested by the Developmental Neuropsychological Assessment II test battery, d2 Test of Attention, and Amsterdam Neuropsychological Tasks. Temperament was assessed by the Temperament in Middle Childhood and Early Adolescent Temperament Questionnaires. Results: The BO sample consisted of 5 BD, 17 UHR, and 41 HR participants. We did not observe any significant differences between the BO and CO groups or between the UHR, HR, and CO subgroups (Hedges' g = 0.21-0.39) in cognitive functioning. The BO differed significantly in some temperament traits from the CO (g = 0.42-0.61), while the UHR subgroup exhibited lower effortful control and attention focusing than both HR and CO participants (g = 0.92-1.19). Limitations: The cross-sectional design and wide age range of the sample limited our findings. Conclusions: Neuropsychological impairment does not seem to be a trait marker of BD in the premorbid stage. Temperament with low effortful control and low attention focusing might be associated with the development of mood disorders in BO.

Cognitive deficits in bipolar disorders: Implications for emotion

Prominent cognitive deficits have been documented in bipolar disorder, and multiple studies suggest that these deficits can be observed among non-affected first-degree relatives of those with bipolar disorder. Although there is variability in the degree of cognitive deficits, these deficits are robustly relevant for functional outcomes. A separate literature documents clear difficulties in emotionality, emotion regulation, and emotion-relevant impulsivity within bipolar disorder, and demonstrates that these emotion-relevant variables are also central to outcome. Although cognitive and emotion domains are typically studied independently, basic research and emergent findings in bipolar disorder suggest that there are important ties between cognitive deficits and the emotion disturbances observed in bipolar disorder. Understanding these relationships has relevance for fostering more integrative research, for clarifying relevant aspects related to functionality and vulnerability within bipolar disorder, and for the development of novel treatment interventions. Bipolar disorder (BD) is a severe psychiatric illness that has been ranked as one of the 20 leading medical causes of disability (WHO, 2011). BD has been shown to be the psychiatric disorder with the highest rates of completed suicide across two major cohort studies (Ilgen et al., 2010; Nordentoft, Mortensen, & Pedersen, 2011). In a cross-national representative sample, one in four persons diagnosed with bipolar I disorder reported a suicide attempt (Merikangas et al., 2011). Rates of relapse remain high despite available treatments (Gitlin, Swendsen, Heller, & Hammen, 1995), and in the year after hospitalization for manic episode, two-thirds of patients do not return to work (Strakowski et al., 1998). Poverty, homelessness, and incarceration are all too common (Copeland et al., 2009). Despite the often poor outcomes, there is also evidence for outstanding accomplishments and creativity among those with milder forms of the disorder and their family members (Coryell et al., 1989; Jamison, 1993; Murray & Johnson, 2010). Some individuals appear to achieve more than the general population, suggesting the importance of understanding the variables that predict differential outcome within bipolar disorder. Within this paper, we focus on two key predictors of outcomes within bipolar disorder: cognition and emotionality. We review evidence that problems in cognition and emotionality are prominent among those diagnosed with the disorder, are not artifacts of symptom state, and relate substantively to poorer outcomes. Although traditionally studied separately, new work points toward the idea that cognition and emotionality are intricately linked within bipolar disorder. Drawing from research within bipolar disorder as well as outside of bipolar disorder, we build a model of how cognition and emotionality might be tied within bipolar disorder. We then provide suggestions for future research. Before considering findings, it is worth noting that there are several forms of the disorder, defined by varying degrees and duration of manic symptoms (APA, 2013; WHO, 1993). Manic episodes are defined by abnormally elevated or irritable mood, accompanied by increased activity and at least three symptoms (four if mood is only irritable) such as decreased need for sleep, increased self-confidence, racing thoughts or flight of ideas, rapid speech, distractibility, goal-directed activity, and engagement in pleasurable activities without regard to potential negative consequences. To meet criteria for mania, these symptoms must persist for at least one week or require hospitalization, and must lead to difficulties with functioning. If functional impairment is not more than mild and duration is between 4 and 6 days, the episode is considered a hypomanic episode. Bipolar I disorder (BD I) is diagnosed on the basis of at least one lifetime manic episode within the DSM-5 and by at least two episodes within the ICD, whereas bipolar II disorder is diagnosed on the basis of at least one hypomanic episode (and no manic episodes) as well as major depressive episodes. Cyclothymic disorder is defined by chronic but milder fluctuations between manic and depressive symptoms. Most research focuses on BD I. In addition to diagnosed samples, research has focused on those at high risk for bipolar disorder, including first-degree relatives of those with BD. This work draws on the evidence for extremely high heritability of BD I, with estimates from community-based twin studies of 0.85 (Kieseppä, Partonen, Haukka, Kaprio, & Lönnqvist, 2014). Other research has considered high risk for BD by virtue of lifetime subsyndromal symptoms, as measured by scales such as the Hypomanic Personality Scale (Eckblad & Chapman, 1986) or the General Behavior Inventory (Depue, Krauss, Spoont, & Arbisi, 1989). The study of high-risk individuals provides a way to decipher whether deficits are present before the onset of the disorder, of importance given models suggesting that episodes of the disorder may change brain function (Chang, Steiner, & Ketter, 2000; Strakowski, 2012) as well as individuals' perceptions of their emotion regulation. Beyond defining BD, it is worth defining some of the many different neuropsychological tasks that have been widely studied in BD. Perhaps no area has received more attention than executive function. Executive function is related to three core functions: 1) inhibition, the ability to suppress irrelevant information in working memory in order to accomplish an established goal; 2) working memory, the ability to hold and manipulate information in mind; and 3) cognitive flexibility, the ability to shift strategies in response to feedback (Diamond, 2013; Miyake et al., 2000). Attention (defined as the process of selecting information reception from internal or external cues) is implicated in all three of these aspects of executive function. Much of the literature we will discuss focuses on response inhibition, or the ability to suppress a prepotent response, which is considered a subtype of inhibition. Some tests measure multiple facets of executive function; for example the Trails B test likely requires working memory and cognitive flexibility (Sánchez-Cubillo et al., 2009). Aside from executive function, multiple other facets of cognition have been widely studied in bipolar disorder. Verbal and non-verbal memory are related to the ability to register, store and retrieve verbal or visual information (Lezak, 1995). Verbal fluency is measured as the number of verbal responses a person can generate to a given target, such as a specific semantic category (e.g., animals, furniture) or phonetic category (e.g., words that begin with letter F) (Diamond, 2013). Although cognitive tasks have been designed to evaluate these specific functions, it is important to note that most measures are highly inter-correlated and may assess multiple overlapping functions to some extent (for example, the Trails B test is often described as an "executive function" task, although this task likely involves both working memory and cognitive flexibility. Not surprisingly, then, some authors label the function of certain tests differently, and this is particularly evident in meta-analyses of cognition. As we describe findings in this paper, we will use the terms proposed by the authors but will also identify key tests used to define a cognitive construct. With this background in mind, we turn to a discussion of cognitive deficits, then of emotion-related traits. Our hope is that those concise summaries provide evidence for the importance of both domains, but also specificity regarding the facets of emotion and cognition that are most impaired in BD. This specificity then guides our consideration of models that integrate cognition and emotion.

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.

The role of white matter in personality traits and affective processing in bipolar disorder

BACKGROUND

Bipolar disorder (BD) is characterized by affective processing bias and variations in personality traits. It is still unknown whether these features are linked to the same structural brain alterations. The aim of this study was to investigate relationships between specific personality traits, white matter (WM) properties, and affective processing in BD and HC.

METHODS

24 healthy controls (HC) and 38 adults with BDI (HC: 29.47 ± 2.23 years, 15 females; BDI: 32.44 ± 1.84 years, 20 females) completed clinical scales and the Big Five Inventory. They were also administered the Affective Go/No-Go (AGN) and the Rapid Visual Processing (RVP) tasks of the Cambridge Neuropsychological Test Automated Battery. Diffusion Tensor Imaging (DTI) assessed the microstructure of WM tracts.

RESULTS

In BDI measures of WM properties were reduced across all major brain white matter tracts. As expected, individuals with BDI reported greater neuroticism, lower agreeableness and conscientiousness, and made a greater number of errors in response to affective stimuli in the AGN task compared to HC. High neuroticism scores were associated with faster AGN latency, and overall reduced AGN accuracy in both HC and BDI. Elevated FA values were associated with reduced neuroticism and increased cognitive processing in HC but not in BDI.

CONCLUSIONS

Our findings showed important potential links between personality, affective processing and WM integrity in BD. In the future therapeutic interventions for BD using brain stimulation protocols might benefit from the use of DTI to target pathways underlying abnormal affective processing.

Neurocognitive functioning in individuals with bipolar disorder and their healthy siblings: A preliminary study

BACKGROUND

Cognitive deficits have been consistently reported in individuals with bipolar disorder (BD). The cognitive profile of siblings of individuals with BD is, however, less clearly established possibly due to the heterogeneity of neuropsychological measures used in previous studies. The aim of this exploratory study was to assess the cognitive function of siblings of individuals with BD and compare it with that of their first-degree relatives suffering with BD, and healthy controls (HC) using the Cambridge Neuropsychological Test Automated Battery (CANTAB) - a comprehensive and validated computerized cognitive battery.

METHODS

We recruited 23 HC (33.52±10.29 years, 8 males), 27 individuals with BD (34.26±10.19 years, 9 males, 25 BDI, 1BDII and 1 BD-NOS), and 15 of their biologically related siblings (37.47±13.15 years, 4 males). Siblings had no current or lifetime history of mental disorders. Participants performed the CANTAB and completed questionnaires assessing mood and global functioning. Multivariate analyses compared CANTAB measures across the three participant groups.

RESULTS

Individuals with BD and their siblings were less accurate in a task of sustained attention (Rapid Visual Processing) when compared to HC. Further, individuals with BD displayed pronounced deficits in affective processing (Affective Go/No-Go) compared to HC. There were no cognitive differences between siblings and individuals with BD. After correcting for current depressive symptoms, these results did not reach statistical significance.

CONCLUSIONS

Subthreshold depressive symptoms may be associated with reduced sustained attention in healthy siblings of BD patients. This preliminary result needs to be corroborated by large-scale, longitudinal studies assessing the relationship between cognition and mood in vulnerable individuals.

Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

BACKGROUND

Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC).

METHODS

A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC.

RESULTS

The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC.

LIMITATIONS

Our study sample was small as we only considered euthymic BD patients and demographically matched HC.

CONCLUSION

Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients.