Emotional cognition subgroups in unaffected first-degree relatives of patients with mood disorders

BACKGROUND

Patients with major depressive disorder (MDD) or bipolar disorder (BD) exhibit difficulties with emotional cognition even during remission. There is evidence for aberrant emotional cognition in unaffected relatives of patients with these mood disorders, but studies are conflicting. We aimed to investigate whether emotional cognition in unaffected first-degree relatives of patients with mood disorders is characterised by heterogeneity using a data-driven approach.

METHODS

Data from 94 unaffected relatives (33 of MDD patients; 61 of BD patients) and 203 healthy controls were pooled from two cohort studies. Emotional cognition was assessed with the Social Scenarios Test, Facial Expression Recognition Test and Faces Dot-Probe Test. Hierarchical cluster analysis was conducted using emotional cognition data from the 94 unaffected relatives. The resulting emotional cognition clusters and controls were compared for emotional and non-emotional cognition, demographic characteristics and functioning.

RESULTS

Two distinct clusters of unaffected relatives were identified: a relatively 'emotionally preserved' cluster (55%; 40% relatives of MDD probands) and an 'emotionally blunted' cluster (45%; 29% relatives of MDD probands). 'Emotionally blunted' relatives presented with poorer neurocognitive performance (global cognition p = 0.010), heightened subsyndromal mania symptoms (p = 0.004), lower years of education (p = 0.004) and difficulties with interpersonal functioning (p = 0.005) than controls, whereas 'emotionally preserved' relatives were comparable to controls on these measures.

CONCLUSIONS

Our findings show discrete emotional cognition profiles that occur across healthy first-degree relatives of patients with MDD and BD. These emotional cognition clusters may provide insight into emotional cognitive markers of genetically distinct subgroups of individuals at familial risk of mood disorders.

Primary teachers' attitudes towards using new technology and stimulating higher-order thinking in students: A profile analysis

Critical thinking, creative thinking, problem solving and other so-called higher-order thinking skills are regarded as crucial for students to develop. Research shows that technology can be used as a tool to stimulate students' higher-order thinking skills. However, most teachers rarely use new technology to stimulate students to engage in higher-order thinking. To help teachers in this, we need to gain an understanding of teachers' attitudes towards using new technology and towards stimulating higher-order thinking. In this study, we explore these teacher attitudes by identifying teacher profiles based on primary school teachers' attitudes (N = 659) towards (a) using new technology and (b) stimulating higher-order thinking. Results of the cluster-analysis revealed three teacher profiles. In follow-up focus group interviews with 21 participants, we found that teachers recognized the identified profiles and that the results of the cluster-analysis matched teachers' self-chosen profiles in almost all cases. These results indicate that we can suitably characterize teachers based on their attitudes towards using new technology and stimulating higher-order thinking. Identification of these profiles may help us understand why certain groups of teachers may use new technology to stimulate students' higher-order thinking, while other teachers might not. This might provide starting points for tailored teacher professionalization for different groups of teachers.

Emotional cognition subgroups in mood disorders: Associations with familial risk

Patients with mood disorders show heterogeneity in non-emotional cognition. However, it is unclear whether emotional cognition (EC) is characterised by similar heterogeneity. We aimed to investigate the heterogeneity in EC among remitted patients with mood disorders and explore its association with familial risk. Data from 269 partially or fully remitted patients with mood disorders, 87 of their unaffected relatives (UR) and 203 healthy controls (HC) were pooled from two cohort studies. Hierarchical cluster analysis was conducted using the EC data from patients. UR were categorised into groups consistent with their affected relatives' cluster assignment. Clusters were compared to HC on EC, non-emotional cognition, clinical characteristics and functioning.  We identified three clusters: an 'emotionally preserved' (57%), an 'emotionally blunted' (26%) and an 'emotionally volatile' cluster (17%). 'Emotionally blunted' and 'emotionally volatile' patients also presented more deficits in non-emotional cognition (global cognition read z=-0.3 and -0.5 respectively). Relatives of 'emotionally preserved' patients were more successful at dampening negative emotions (p=.01, d=0.39, 95% CI [-0.76,-0.09]), whereas UR of 'emotionally impaired' patients underperformed in verbal fluency (p=.03, d=0.46, 95% CI [.03, 0.68]) compared to HC. The existence of impaired EC groups in remitted mood disorder highlights a need to screen for and treat EC in mood disorders. Improved ability to dampen emotions in UR of 'emotionally preserved' patients may reflect a resilience marker while impaired verbal fluency in UR of 'emotionally impaired' patients may reflect distinct genetic risk profiles in these EC subgroups.

Discrimination of nano-objects via cluster analysis techniques applied to time-resolved thermo-acoustic microscopy

Time-effective, unsupervised clustering techniques are exploited to discriminate nanometric metal disks patterned on a dielectric substrate. The discrimination relies on cluster analysis applied to time-resolved optical traces obtained from thermo-acoustic microscopy based on asynchronous optical sampling. The analysis aims to recognize similarities among nanopatterned disks and to cluster them accordingly. Each cluster is characterized by a fingerprint time-resolved trace, synthesizing the common features of the thermo-acoustics response of the composing elements. The protocol is robust and widely applicable, not relying on any specific knowledge of the physical mechanisms involved. The present route constitutes an alternative diagnostic tool for on-chip non-destructive testing of individual nano-objects.

Identifying individuals with chronic pain after knee replacement: a population-cohort, cluster-analysis of Oxford knee scores in 128,145 patients from the English National Health Service

BACKGROUND

Approximately one in five patients undergoing knee replacement surgery experience chronic pain after their operation, which can negatively impact on their quality of life. In order to develop and evaluate interventions to improve the management of chronic post-surgical pain, we aimed to derive a cut-off point in the Oxford Knee Score pain subscale to identify patients with chronic pain following knee replacement, and to characterise these patients using self-reported outcomes.

METHODS

Data from the English Patient-Reported Outcome Measures (PROMs) programme were used. This comprised patient-reported data from 128,145 patients who underwent primary knee replacement surgery in England between 2012 and 2015. Cluster analysis was applied to derive a cut-off point on the pain subscale of the Oxford Knee Score.

RESULTS

A high-pain group was identified, described by a maximum of 14 points in the Oxford Knee Score pain subscale six months after surgery. The high-pain group, comprising 15% of the sample, was characterised by severe and frequent problems in all pain dimensions, particularly in pain severity, night pain and limping, as well as in all dimensions of health-related quality of life.

CONCLUSIONS

Patients with Oxford Knee Score pain subscale scores of 14 or less at six months after knee replacement can be considered to be in chronic pain that is likely to negatively affect their quality of life. This derived cut-off can be used for patient selection in research settings to design and assess interventions that support patients in their management of chronic post-surgical pain.

Serious juvenile offenders: classification into subgroups based on static and dynamic charateristics

BACKGROUND

The population in juvenile justice institutions is heterogeneous, as juveniles display a large variety of individual, psychological and social problems. This variety of risk factors and personal characteristics complicates treatment planning. Insight into subgroups and specific profiles of problems in serious juvenile offenders is helpful in identifying important treatment indicators for each subgroup of serious juvenile offenders.

METHODS

To identify subgroups with combined offender characteristics, cluster-analyses were performed on data of 2010 adolescents from all juvenile justice institutions in the Netherlands. The study included a wide spectrum of static and dynamic offender characteristics and was a replication of a previous study, in order to replicate and validate the identified subgroups. To identify the subgroups that are most useful in clinical practice, different numbers of subgroup-solutions were presented to clinicians.

RESULTS

Combining both good statistical fit and clinical relevance resulted in seven subgroups. Most subgroups resemble the subgroups found in the previous study and one extra subgroups was identified. Subgroups were named after their own identifying characteristics: (1) sexual problems, (2) antisocial identity and mental health problems, (3) lack of empathy and conscience, (4) flat profile, (5) family problems, (6) substance use problems, and (7) sexual, cognitive and social problems.

CONCLUSIONS

Subgroups of offenders as identified seem rather stable. Therefore risk factor scores can help to identify characteristics of serious juvenile offenders, which can be used in clinical practice to adjust treatment to the specific risk and needs of each subgroup.

Sample heterogeneity in unipolar depression as assessed by functional connectivity analyses is dominated by general disease effects

OBJECTIVES

Combinations of resting-state fMRI and machine-learning techniques are increasingly employed to develop diagnostic models for mental disorders. However, little is known about the neurobiological heterogeneity of depression and diagnostic machine learning has mainly been tested in homogeneous samples. Our main objective was to explore the inherent structure of a diverse unipolar depression sample. The secondary objective was to assess, if such information can improve diagnostic classification.

MATERIALS AND METHODS

We analyzed data from 360 patients with unipolar depression and 360 non-depressed population controls, who were subdivided into two independent subsets. Cluster analyses (unsupervised learning) of functional connectivity were used to generate hypotheses about potential patient subgroups from the first subset. The relationship of clusters with demographical and clinical measures was assessed. Subsequently, diagnostic classifiers (supervised learning), which incorporated information about these putative depression subgroups, were trained.

RESULTS

Exploratory cluster analyses revealed two weakly separable subgroups of depressed patients. These subgroups differed in the average duration of depression and in the proportion of patients with concurrently severe depression and anxiety symptoms. The diagnostic classification models performed at chance level.

LIMITATIONS

It remains unresolved, if subgroups represent distinct biological subtypes, variability of continuous clinical variables or in part an overfitting of sparsely structured data.

CONCLUSIONS

Functional connectivity in unipolar depression is associated with general disease effects. Cluster analyses provide hypotheses about potential depression subtypes. Diagnostic models did not benefit from this additional information regarding heterogeneity.

Too strict or too loose? Perfectionism and impulsivity: the relation with eating disorder symptoms using a person-centered approach

Although both perfectionism (i.e. personal standards perfectionism and evaluative concerns perfectionism) and impulsivity have been shown to be implicated in eating disorders, no previous studies have examined the interplay between both personality dimensions in their association with eating disorder symptoms. This is the first study to investigate the relationship between empirically derived personality subtypes based on perfectionism and impulsivity and eating disorder symptoms (i.e., dietary restraint, and concerns over eating, weight and shape). Cluster analysis was used to establish naturally occurring combinations of perfectionism and impulsivity in adolescent boys and girls (N=460; M age=14.2 years, SD=.90). Evidence was obtained for four personality profiles: (1) a resilient subtype (low on perfectionism and impulsivity), (2) pure impulsivity subtype (high on impulsivity only), (3) pure perfectionism subtype (high on perfectionism only), and (4) combined perfectionism/impulsivity subtype (high on both perfectionism and impulsivity). Participants in these four clusters showed differences in terms of eating disorder symptoms in that participants with a combination of high perfectionism and high impulsivity (rather than the presence of one of these two characteristics alone) had the highest levels of ED symptoms. These findings shed new light on extant theories concerning ED.