Theory of Mind and social functioning among neuropsychiatric disorders: A transdiagnostic study.

Social dysfunction is commonly present in neuropsychiatric disorders of schizophrenia (SZ) and Alzheimer's disease (AD). Theory of Mind (ToM) deficits have been linked to social dysfunction in disease-specific studies. Nevertheless, it remains unclear how ToM is related to social functioning across these disorders, and which factors contribute to this relationship. We investigated transdiagnostic associations between ToM and social functioning among SZ/AD patients and healthy controls, and explored to what extent these associations relate to information processing speed or facial emotion recognition capacity. A total of 163 participants were included (SZ: n=56, AD: n=50 and age-matched controls: n=57). Social functioning was assessed with the Social Functioning Scale (SFS) and the De Jong-Gierveld Loneliness Scale (LON). ToM was measured with the Hinting Task. Information processing speed was measured by the Digit Symbol Substitution Test (DSST) and facial emotion recognition capacity by the facial emotion recognition task (FERT). Case-control deficits in Hinting Task performance were larger in AD (rrb = -0.57) compared to SZ (rrb = -0.35). Poorer Hinting Task performance was transdiagnostically associated with the SFS (ßHinting-Task = 1.20, p<0.01) and LON (ßHinting-Task = -0.27, p<0.05). DSST, but not FERT, reduced the association between the SFS and Hinting Task performance, however the association remained significant (ßHinting-Task = 0.95, p<0.05). DSST and FERT performances did not change the association between LON and Hinting Task performance. Taken together, ToM deficits are transdiagnostically associated with social dysfunction and this is partly related to reduced information processing speed.

Actigraphic patterns, impulsivity and mood instability in bipolar disorder, borderline personality disorder and healthy controls.

OBJECTIVES: To differentiate the relation between the structure and timing of rest-activity patterns and symptoms of impulsivity and mood instability in bipolar disorder (BD), borderline personality disorder (BPD) and healthy controls (HC). METHODS: Eighty-seven participants (31 BD, 21 BPD and 35 HC) underwent actigraph monitoring for 28 days as part of the Automated Monitoring of Symptom Severity (AMoSS) study. Impulsivity was assessed at study entry using the BIS-11. Mood instability was subsequently longitudinally monitored using the digital Mood Zoom questionnaire. RESULTS: BPD participants show several robust and significant correlations between non-parametric circadian rest-activity variables and worsened symptoms. Impulsivity was associated with low interdaily stability (r = -0.663) and weak amplitude (r = -0.616). Mood instability was associated with low interdaily stability (r = -0.773), greater rhythm fragmentation (r = 0.662), weak amplitude (r = -0.694) and later onset of daily activity (r = 0.553). These associations were not present for BD or HCs. Classification analysis using actigraphic measures determined that later L5 onset reliably distinguished BPD from BD and HC but did not sufficiently discriminate between BD and HC. CONCLUSIONS: Rest-activity pattern disturbance indicative of perturbed sleep and circadian function is an important predictor of symptom severity in BPD. This appears to validate the greater subjective complaints of BPD individuals that are sometimes regarded as exaggerated by clinicians. We suggest that treatment strategies directed towards improving sleep and circadian entrainment may in the future be investigated in BPD.

Variability in phase and amplitude of diurnal rhythms is related to variation of mood in bipolar and borderline personality disorder.

Variable mood is an important feature of psychiatric disorders. However, its measurement and relationship to objective measureas of physiology and behaviour have rarely been studied. Smart-phones facilitate continuous personalized prospective monitoring of subjective experience and behavioural and physiological signals can be measured through wearable devices. Such passive data streams allow novel estimates of diurnal variability. Phase and amplitude of diurnal rhythms were quantified using new techniques that fitted sinusoids to heart rate (HR) and acceleration signals. We investigated mood and diurnal variation for four days in 20 outpatients with bipolar disorder (BD), 14 with borderline personality disorder (BPD) and 20 healthy controls (HC) using a smart-phone app, portable electrocardiogram (ECG), and actigraphy. Variability in negative affect, positive affect, and irritability was elevated in patient groups compared with HC. The study demonstrated convincing associations between variability in subjective mood and objective variability in diurnal physiology. For BPD there was a pattern of positive correlations between mood variability and variation in activity, sleep and HR. The findings suggest BPD is linked more than currently believed with a disorder of diurnal rhythm; in both BPD and BD reducing the variability of sleep phase may be a way to reduce variability of subjective mood.

Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group.

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain magnetic resonance imaging scans of 6503 individuals including 1837 unrelated adults with BD and 2582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen's d=-0.293; P=1.71 × 10-21), left fusiform gyrus (d=-0.288; P=8.25 × 10-21) and left rostral middle frontal cortex (d=-0.276; P=2.99 × 10-19). Longer duration of illness (after accounting for age at the time of scanning) was associated with reduced cortical thickness in frontal, medial parietal and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. We found evidence of reduced cortical surface area associated with a history of psychosis but no associations with mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD.

Detecting Bipolar Depression From Geographic Location Data.

OBJECTIVE: This paper aims to identify periods of depression using geolocation movements recorded from mobile phones in a prospective community study of individuals with bipolar disorder (BD). METHODS: Anonymized geographic location recordings from 22 BD participants and 14 healthy controls (HC) were collected over 3 months. Participants reported their depressive symptomatology using a weekly questionnaire (QIDS-SR16). Recorded location data were preprocessed by detecting and removing imprecise data points and features were extracted to assess the level and regularity of geographic movements of the participant. A subset of features were selected using a wrapper feature selection method and presented to 1) a linear regression model and a quadratic generalized linear model with a logistic link function for questionnaire score estimation; and 2) a quadratic discriminant analysis classifier for depression detection in BD participants based on their questionnaire responses. R esults: HC participants did not report depressive symptoms and their features showed similar distributions to nondepressed BD participants. Questionnaire score estimation using geolocation-derived features from BD participants demonstrated an optimal mean absolute error rate of 3.73, while depression detection demonstrated an optimal (median ± IQR) [Formula: see text] score of 0.857 ± 0.022 using five features (classification accuracy: 0.849 ± 0.016; sensitivity: 0.839 ± 0.014; specificity: 0.872 ± 0.047). CONCLUSION: These results demonstrate a strong link between geographic movements and depression in bipolar disorder. S ignificance: To our knowledge, this is the first community study of passively recorded objective markers of depression in bipolar disorder of this scale. The techniques could help individuals monitor their depression and enable healthcare providers to detect those in need of care or treatment.

Experiences of remote mood and activity monitoring in bipolar disorder: A qualitative study.

BACKGROUND: Mobile technology enables high frequency mood monitoring and automated passive collection of data (e.g. actigraphy) from patients more efficiently and less intrusively than has previously been possible. Such techniques are increasingly being deployed in research and clinical settings however little is known about how such approaches are experienced by patients. Here, we explored the experiences of individuals with bipolar disorder engaging in a study involving mood and activity monitoring with a range of portable and wearable technologies. METHOD: Patients were recruited from a wider sample of 50 individuals with Bipolar Disorder taking part in the Automated Monitoring of Symptom Severity (AMoSS) study in Oxford. A sub-set of 21 patients participated in a qualitative interview that followed a semi-structured approach. RESULTS: Monitoring was associated with benefits including increased illness insight, behavioural change. Concerns were raised about the potential preoccupation with, and paranoia about, monitoring. Patients emphasized the need for personalization, flexibility, and the importance of context, when monitoring mood. CONCLUSIONS: Mobile and electronic health approaches have potential to lend new insights into mental health and transform healthcare. Capitalizing on the perceived utility of these approaches from the patients' perspective, while addressing their concerns, will be essential for the promise of new technologies to be realised.

Associations between mood instability and emotional processing in a large cohort of bipolar patients.

BACKGROUND: Aberrant emotional biases have been reported in bipolar disorder (BD), but results are inconsistent. Despite the clinical relevance of chronic mood variability in BD, there is no previous research investigating how the extent of symptom fluctuations in bipolar disorder might relate to emotional biases. This exploratory study investigated, in a large cohort of bipolar patients, whether instability in weekly mood episode symptoms and other clinical and demographic factors were related to emotional bias as measured in a simple laboratory task. METHOD: Participants (N = 271, BDI = 206, BDII = 121) completed an 'emotional categorization and memory' task. Weekly self-reported symptoms of depression and mania were collected prospectively. In linear regression analyses, associations between cognitive bias and mood variability were explored together with the influence of demographic and clinical factors, including current medication. RESULTS: Greater accuracy in the classification of negative words relative to positive words was associated with greater instability in depressive symptoms. Furthermore, greater negative bias in free recall was associated with higher instability in manic symptoms. Participants diagnosed with BDII, compared with BDI, showed overall better word recognition and recall. Current antipsychotic use was associated with reduced instability in manic symptoms but this did not impact on emotional processing performance. CONCLUSIONS: Emotional processing biases in bipolar disorder are related to instability in mood. These findings prompt further investigation into the underpinnings as well as clinical significance of mood instability.

Daily longitudinal self-monitoring of mood variability in bipolar disorder and borderline personality disorder.

BACKGROUND: Traditionally, assessment of psychiatric symptoms has been relying on their retrospective report to a trained interviewer. The emergence of smartphones facilitates passive sensor-based monitoring and active real-time monitoring through time-stamped prompts; however there are few validated self-report measures designed for this purpose. METHODS: We introduce a novel, compact questionnaire, Mood Zoom (MZ), embedded in a customised smart-phone application. MZ asks participants to rate anxiety, elation, sadness, anger, irritability and energy on a 7-point Likert scale. For comparison, we used four standard clinical questionnaires administered to participants weekly to quantify mania (ASRM), depression (QIDS), anxiety (GAD-7), and quality of life (EQ-5D). We monitored 48 Bipolar Disorder (BD), 31 Borderline Personality Disorders (BPD) and 51 Healthy control (HC) participants to study longitudinal (median±iqr: 313±194 days) variation and differences of mood traits by exploring the data using diverse time-series tools. RESULTS: MZ correlated well (|R|>0.5,p<0.0001) with QIDS, GAD-7, and EQ-5D. We found statistically strong (|R|>0.3,p<0.0001) differences in variability in all questionnaires for the three cohorts. Compared to HC, BD and BPD participants exhibit different trends and variability, and on average had higher self-reported scores in mania, depression, and anxiety, and lower quality of life. In particular, analysis of MZ variability can differentiate BD and BPD which was not hitherto possible using the weekly questionnaires. LIMITATIONS: All reported scores rely on self-assessment; there is a lack of ongoing clinical assessment by experts to validate the findings. CONCLUSIONS: MZ could be used for efficient, long-term, effective daily monitoring of mood instability in clinical psychiatric practice.

Distinguishing bipolar disorder from borderline personality disorder: A study of current clinical practice.

BACKGROUND: Diagnosing mental illness is a central role for psychiatrists. Correct diagnosis informs both treatment and prognosis, and facilitates accurate communication. We sought to explore how psychiatrists distinguished two common psychiatric diagnoses: bipolar disorder (BD) and borderline personality disorder (BPD). METHODS: We conducted a qualitative study of psychiatrists to explore their practical experience. We then sought to validate these results by conducting a questionnaire study testing the theoretical knowledge and practical experience of a large number of UK psychiatrists. Finally we studied the assessment process in NHS psychiatric teams by analysing GP letters, assessments by psychiatrists, and assessment letters. RESULTS: There was broad agreement in both the qualitative and questionnaire studies that the two diagnoses can be difficult to distinguish. The majority of psychiatrists demonstrated in survey responses a comprehensive understanding DSM-IV-TR criteria although many felt that these criteria did not necessarily assist diagnostic differentiation. This scepticism about diagnostic criteria appeared to strongly influence clinical practice in the sample of clinicians we observed. In only a minority of assessments were symptoms of mania or BPD sufficiently assessed to establish the presence or absence of each diagnosis. CONCLUSION: Clinical diagnostic practice was not adequate to differentiate reliably BD and BPD. The absence of reliable diagnostic practice has widespread implications for patient care, service provision and the reliability of clinical case registries.