Using network analysis to explore cognitive domains in patients with unipolar versus bipolar depression: a prospective naturalistic study

The THINC-it tool: temporal sensitivity to change over time

INTRODUCTION

Cognitive dysfunction is believed to be among the core features of Bipolar Depression(BD-D). However, its evaluation and available treatments are limited. Here, we conducted a longitudinal follow-up clinical trial using the THINC-it tool to evaluate temporal sensitivity to change over time in cognitive function among patients with bipolar depression from a Chinese cohort. It is helpful to verify whether the scale can continuously and reliably measure cognitive function in different time points and reduce the measurement error caused by time factors. Hope our findings could provide insights into the significance of the THINC-it tool as an iterative clinical cognitive evaluation tool.

METHODS

A total of 120 patients with bipolar depression(40 males and 80 females, respectively) alongside 100 healthy controls(23 males and 77 females, respectively) were recruited in the study. All participants were interviewed for 8 weeks, using the 17-item Hamilton Depression Rating Scale (HAMD-17) and the Young Mania Rating Scale(YMRS). The primary dependent measure was the previously validated THINC-it tool, followed by psychometric analysis.

RESULTS

Repeated measures of the THINC-it tool at baseline, one-week, and eight-week periods were conducted after controlling for age, gender, and education effects. Results from the general linear model revealed no significant time differences in variances(P > 0.05). Similarly, adjusting for confounding factors (age, gender, education, and HAMD-17 scores), results from the longitudinal analysis showed that there were no significant differences in cognitive impairment over time(P > 0.05). However, we found significant differences between BD-D and Healthy Control(HC) groups with regards to Spotter, Codebreaker, Trails, Perceived Deficits Questionnaire for Depression-5-items(PDQ-5-D), and THINC-it Total composite(P < 0.05), but not in Symbol Check (p = 0.191).

CONCLUSION

These findings indicate that the THINC-it tool effectively detects sensitivity to change in groups and maintains stability at times, indicating that it is a feasible and reliable instrument for evaluating cognitive dysfunction in Chinese patients with bipolar depression.

Network dysfunction of sadness facial expression processing and morphometry in euthymic bipolar disorder

Facial emotion recognition (FER), including sadness, is altered in bipolar disorder (BD). However, the relationship between this impairment and the brain structure in BD is relatively unexplored. Furthermore, its association with clinical variables and with the subtypes of BD remains to be clarified. Twenty euthymic patients with BD type I (BD-I), 28 BD type II (BD-II), and 45 healthy controls completed a FER test and a 3D-T1-weighted magnetic resonance imaging. Gray matter volume (GMV) of the cortico-limbic regions implicated in emotional processing was estimated and their relationship with FER performance was investigated using network analysis. Patients with BD-I had worse total and sadness-related FER performance relative to the other groups. Total FER performance was significantly negatively associated with illness duration and positively associated with global functioning in patients with BD-I. Sadness-related FER performance was also significantly negatively associated with the number of previous manic episodes. Network analysis showed a reduced association of the GMV of the frontal-insular-occipital areas in patients with BD-I, with a greater edge strength between sadness-related FER performance and amygdala GMV relative to controls. Our results suggest that FER performance, particularly for facial sadness, may be distinctively impaired in patients with BD-I. The pattern of reduced interrelationship in the frontal-insular-occipital regions and a stronger positive relationship between facial sadness recognition and the amygdala GMV in BD may reflect altered cortical modulation of limbic structures that ultimately predisposes to emotional dysregulation. Future longitudinal studies investigating the effect of mood state on FER performance in BD are warranted.

Identifying the bridge between depression and mania: A machine learning and network approach to bipolar disorder

OBJECTIVES

Although the cyclic nature of bipolarity is almost by definition a network system, no research to date has attempted to scrutinize the relationship of the two bipolar poles using network psychometrics. We used state-of-the-art network and machine learning methodologies to identify symptoms, as well as relations thereof, that bridge depression and mania.

METHODS

Observational study that used mental health data (12 symptoms for depression and 12 for mania) from a large, representative Canadian sample (the Canadian Community Health Survey of 2002). Complete data (N = 36,557; 54.6% female) were analysed using network psychometrics, in conjunction with a random forest algorithm, to examine the bidirectional interplay of depressive and manic symptoms.

RESULTS

Centrality analyses pointed to symptoms relating to emotionality and hyperactivity as being the most central aspects of depression and mania, respectively. The two syndromes were spatially segregated in the bipolar model and four symptoms appeared crucial in bridging them: sleep disturbances (insomnia and hypersomnia), anhedonia, suicidal ideation, and impulsivity. Our machine learning algorithm validated the clinical utility of central and bridge symptoms (in the prediction of lifetime episodes of mania and depression), and suggested that centrality, but not bridge, metrics map almost perfectly onto a data-driven measure of diagnostic utility.

CONCLUSIONS

Our results replicate key findings from past network studies on bipolar disorder, but also extend them by highlighting symptoms that bridge the two bipolar poles, while also demonstrating their clinical utility. If replicated, these endophenotypes could prove fruitful targets for prevention/intervention strategies for bipolar disorders.

A cross-lagged prospective network analysis of depression and anxiety and cognitive functioning components in midlife community adult women

BACKGROUND

Scar theory proposes that heightened depression and anxiety precede and predict worse cognitive functioning outcomes, whereas the vulnerability theory posits the opposite pathway. However, most investigations on this topic have been cross-sectional, precluding causal inferences. Thus, we used cross-lagged prospective network analyses to facilitate causal inferences in understanding the relations between psychopathology and cognitive functioning components.

METHODS

Racially-diverse midlife women (n = 1816) participated in the Study of Women's Health Across the Nation at two time-points, spanning one year apart. Five psychopathology (anxiety severity, depressed mood, somatic symptoms, positive affect, interpersonal problems) and four cognitive functioning nodes (working memory (WM), processing speed (PS), facial recognition (FCR), and verbal memory (VRM)) were assessed. All analyses adjusted for age, menopausal status, estradiol, and follicle-stimulating hormones.

RESULTS

Contemporaneous networks yielded notable inverse between-node relations (edges) between interpersonal problems and reduced FCR and PS, and between depressed mood and lower FCR, VRM, or PS. Nodes that had the highest likelihood to bridge other constructs were positive affect, anxiety severity, WM, and VRM. Temporal networks produced edges consistent with the scar (v. vulnerability) hypotheses. Higher somatic symptoms were related to reduced PS and WM, and greater depressed mood was correlated with lower future PS and WM. Also, higher anxiety severity coincided with decreased future PS and WM. Greater positive affect was associated with stronger future PS, FCR, and WM. Also, positive affect had the strongest relations with other nodes.

CONCLUSIONS

Findings suggest the importance of targeting symptoms and cognitive functioning simultaneously.

Variation in self and familiar facial recognition in bipolar disorder patients at different clinical stages

Previous studies suggest a close relationship between self-disorders and schizophrenia or unipolar depression. However, few studies have explored the characteristics of self-processing in bipolar disorder (BD) during different clinical states. This study compared the differences in self-face recognition (SFR) among patients with bipolar mania (BPM), bipolar depression (BPD), bipolar remission (RM), and healthy controls (HC). Images of subject's own face, a familiar face, and an unfamiliar face were combined in pairs at a certain proportion to obtain three types of blended images. We then compared the tendency between BD and HC while judging two kinds of blended faces emerging from presentation software. The results showed that the BPM and BPD groups seemed to lack an advantage in self-recognition. Self-processing and familiarity processing were significantly enhanced in BPM patients, while only familiarity processing was enhanced in BPD. The severity of clinical symptoms was not significantly correlated with self-bias or familiarity bias in BD.

Predictors of functional outcome in patients with major depression and bipolar disorder: A dynamic network approach to identify distinct patterns of interacting symptoms

The purpose of this study is to use a dynamic network approach as an innovative way to identify distinct patterns of interacting symptoms in patients with Major Depressive Disorder (MDD) and patients with Bipolar Type I Disorder (BD). More precisely, the hypothesis will be testing that the phenotype of patients is driven by disease specific connectivity and interdependencies among various domains of functioning even in the presence of underlying common mechanisms. In a prospective observational cohort study, hundred-forty-three patients were recruited at the Psychiatric Clinic "Villa dei Gerani" (Catania, Italy), 87 patients with MDD and 56 with BD with a depressive episode. Two nested sub-groups were treated for a twelve-week period, which allowed us to explore differences in the pattern of symptom distribution (central vs. peripheral) and their connectedness (strong vs weak) before (T0) and after (T1) treatment. All patients underwent a complete neuropsychological evaluation at baseline (T0) and at T1. A network structure was computed for MDD and BD patients at T0 and T1 from a covariance matrix of 17 items belonging to three domains-neurocognitive, psychosocial, and mood-related (affective) to identify what symptoms were driving the networks. Clinically relevant differences were observed between MDD and BD, at T0 and after 12 weeks of pharmacological treatment. At time T0, MDD patients displayed an affective domain strongly connected with the nodes of psychosocial functioning, while direct connectivity of the affective domain with the neurocognitive cluster was absent. The network of patients with BD, in contrast, revealed a cluster of highly interconnected psychosocial nodes but was guided by neurocognitive functions. The nodes related to the affective domain in MDD are less connected and placed in the periphery of the networks, whereas in BD they are more connected with psychosocial and neurocognitive nodes. Noteworthy is that, from T0 to T1 the "Betweenness" centrality measure was lower in both disorders which means that fewer "shortest paths" between nodes pass through the affective domain. Moreover, fewer edges were connected directly with the nodes in this domain. In MDD patients, pharmacological treatment primarily affected executive functions which seem to improve with treatment. In contrast, in patients with BD, treatment resulted in improvement of overall connectivity and centrality of the affective domain, which seems then to affect and direct the overall network. Though different network structures were observed for MDD and BD patients, data suggest that treatment should include tailored cognitive therapy, because improvement in this central domain appeared to be fundamental for better outcomes in other domains. In sum, the advantage of network analysis is that it helps to predict the trajectory of future phenotype related disease manifestations. In turn, this allows new insights in how to balance therapeutic interventions, involving different fields of function and combining pharmacological and non-pharmacological treatment modalities.

Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study

BACKGROUND

Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments.

METHODS

Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis.

RESULTS

Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed.

CONCLUSIONS

Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Elevated Anxious and Depressed Mood Relates to Future Executive Dysfunction in Older Adults: A Longitudinal Network Analysis of Psychopathology and Cognitive Functioning

Vulnerability models posit that executive-functioning (EF) problems centrally affect future common (vs. rare) psychopathology symptoms. Conversely, scar theory postulates that depression/anxiety (vs. other psychopathology) symptoms centrally influence reduced EF. However, most studies so far have been cross-sectional. We used cross-lagged panel network analysis to determine temporal and component-to-component relations on this topic. Community older adults participated across four time points. Cognitive tests and the caregiver-rated Neuropsychiatric Inventory assessed nine psychopathology and eight cognitive-functioning nodes. Nodes with the highest bridge expected influence cross-sectionally were agitation and episodic memory. Episodic memory had the strongest inverse relation with age. Agitation had the strongest negative association with global cognition. EF nodes tended to be centrally affected by prior depressed and anxious moods rather than influential on any future nodes. Heightened anxious and depressed mood (vs. other nodes) centrally predicted future decreased EF-related (vs. non-EF-related) nodes in older adults, supporting scar (vs. vulnerability) theory.

Aberrant Functional Connectivity of Sensorimotor Network and Its Relationship With Executive Dysfunction in Bipolar Disorder Type I

Background

The key pathophysiological mechanism of executive dysfunction in patients with bipolar disorder type I (BD-I) is still unclear. Previous studies have demonstrated that it may be related to the disbalance of the sensory motor network (SMN).

Objective

This study was designed to explore the aberrant functional connectivity (FC) of SMN in BD-I patients and its potential associations with executive dysfunction.

Methods

Eighteen BD-I patients and 20 healthy controls (HCs) underwent resting-state fMRI scans. The intranetwork and internetwork functional connectivities of SMN were extracted by independent component analysis (ICA). Clinical symptoms were assessed by the Bech–Rafaelsen Mania Rating Scale (BRMS) and Positive and Negative Syndrome Scale (PANSS). Executive function was measured by digit span tasks and a verbal fluency test. Finally, linear regression and correlation analyses were applied to measure the potential associations between clinical symptoms, intranetwork and internetwork functional connectivities, and executive function performance.

Results

(1) Patients with BD-I showed increased connectivity in the right paracentral lobule and the right postcentral gyrus within the SMN, and the increased connectivity value was positively correlated with the BRMS score (P < 0.05) but negatively correlated with digit span forward scores (P < 0.05). (2) Compared with HC, the connectivity value increased between the SMN and dorsal attention network (DAN) (P < 0.01) and between the default mode network (DMN) and DAN (P < 0.05) but decreased between the DAN and auditory network (AN) (P < 0.05) and between the SMN and DMN (P < 0.01) in patients with BD-I. (3) Digit span forward scores and education of all participants were negatively correlated with FC between SMN and DAN. Age of all subjects was positively correlated with FC between SMN and DMN.

Conclusion

Our findings suggest that the sensorimotor network of BD-I has abnormal functional connections within and between networks, and the abnormal FC value correlated with clinical symptoms and executive function, which provide new information for exploring the neural physiopathology of executive dysfunction in BD-I patients.

Can network analysis of self-reported psychopathology shed light on the core phenomenology of bipolar disorders in adolescents and young adults?

OBJECTIVES

Network analysis is increasingly applied to psychopathology research. We used it to examine the core phenomenology of emerging bipolar disorder (BD I and II) and 'at risk' presentations (major depression with a family history of BD).

METHODOLOGY

The study sample comprised a community cohort of 1867 twin and nontwin siblings (57% female; mean age ~26) who had completed self-report ratings of (i) depression-like, hypomanic-like and psychotic-like experiences; (ii) family history of BD; and (iii) were assessed for mood and psychotic syndromes using the Composite International Diagnostic Interview (CIDI). Symptom networks were compared for recent onset BD versus other cohort members and then for individuals at risk of BD (depression with/without a family history of BD).

RESULTS

The four key symptoms that differentiated recent onset BD from other cohort members were: anergia, psychomotor speed, hypersomnia and (less) loss of confidence. The four key symptoms that differentiated individuals at high risk of BD from unipolar depression were anergia, psychomotor speed, impaired concentration and hopelessness. However, the latter network was less stable and more error prone.

CONCLUSIONS

We are encouraged by the overlaps between our findings and those from two recent publications reporting network analyses of BD psychopathology, especially as the studies recruited from different populations and employed different network models. However, the advantages of applying network analysis to youth mental health cohorts (which include many individuals with multimorbidity) must be weighed against the disadvantages including basic issues such as judgements regarding the selection of items for inclusion in network models.

The Association Between Internet Addiction and Anxiety in Nursing Students: A Network Analysis

Background: Nursing students who suffer from co-occurring anxiety experience added difficulties when communicating and interacting with others in a healthy, positive, and meaningful way. Previous studies have found strong positive correlations between Internet addiction (IA) and anxiety, suggesting that nursing students who report severe IA are susceptible to debilitating anxiety as well. To date, however, network analysis (NA) studies exploring the nature of association between individual symptoms of IA and anxiety have not been published. Objective: This study examined associations between symptoms of IA and anxiety among nursing students using network analysis. Methods: IA and anxiety symptoms were assessed using the Internet Addiction Test (IAT) and the Generalized Anxiety Disorder Screener (GAD-7), respectively. The structure of IA and anxiety symptoms was characterized using "Strength" as a centrality index in the symptom network. Network stability was tested using a case-dropping bootstrap procedure and a Network Comparison Test (NCT) was conducted to examine whether network characteristics differed on the basis of gender and by region of residence. Results: A total of 1,070 nursing students participated in the study. Network analysis showed that IAT nodes, "Academic decline due to Internet use," "Depressed/moody/nervous only while being off-line," "School grades suffer due to Internet use," and "Others complain about your time spent online" were the most influential symptoms in the IA-anxiety network model. Gender and urban/rural residence did not significantly influence the overall network structure. Conclusion: Several influential individual symptoms including Academic declines due to Internet use, Depressed/moody/nervous only while being off-line, School grades suffering due to Internet use and Others complain about one's time spent online emerged as potential targets for clinical interventions to reduce co-occurring IA and anxiety. Additionally, the overall network structure provides a data-based hypothesis for explaining potential mechanisms that account for comorbid IA and anxiety.

Effectiveness, tolerability, and dropout rates of vortioxetine in comorbid depression: A naturalistic study

OBJECTIVE

Vortioxetine is a novel antidepressant whose safety, tolerability, and therapeutic action have been supported by several studies. The present naturalistic study aimed to characterize its effectiveness, tolerability, and dropout rate in the real world.

METHODS

Total sample consisted of 66 outpatients with major depressive episode, treated with vortioxetine, whose clinical variables were evaluated over three time points.

RESULTS

Most common primary diagnoses were major depressive disorder (45.5%) and bipolar disorder (33.4%), with an overall comorbidity rate of 48.5% and concomitant medications in the 89.4%. The mean vortioxetine daily dosage was 12.90 ± 5.65 mg. Effectiveness of vortioxetine through a significant improvement on specific psychometric scales emerged, while only a nonsignificant trend of association between higher dosage and effectiveness was found. In the total sample, 51.5% were classified as responders and 36.4% as remitters. Two-thirds of subjects did not report side effects, while in the remaining patients, gastrointestinal ones were the most frequent (72.7%). Almost two-thirds of the sample could complete the follow-up, while 36.4% dropped out; the main reasons for dropout were side effects (37.5%) and lack of efficacy (29.2%).

CONCLUSIONS

Larger sample studies are warranted to better characterize vortioxetine effectiveness and tolerability in the real world.

Early Age of Migraine Onset is Independently Related to Cognitive Decline and Symptoms of Depression Affect Quality of Life

Background

People with migraine experience cognitive decline more often than healthy controls, resulting in a significant functional impact. Early identifying influencing factors that contribute to cognitive decline in migraineurs is crucial for timely intervention. Although migraine may onset early in childhood and early onset migraine is related to significant disability, there is no research investigating the association between the age of migraine onset and migraineurs’ cognitive decline. Therefore we aim to explore possible factors that correlate to the cognitive function of migraineurs, especially focus on age of migraine onset.

Methods

531 patients with migraine were included. Data on demographics and headache-related characteristics were collected and evaluated using face-to-face interviews and questionnaires. We used the Montreal Cognitive Assessment scale to assess cognitive function. In addition, we analyzed independent correlations between cognitive decline and the age of migraine onset in patients with migraine. And all patients completed the Headache Impact Test-6 to evaluate their quality of life.

Results

Migraineurs with cognitive decline showed significant differences from those without in age (OR=1.26, P<0.0001), years of education (OR=0.89, P=0.0182), the intensity of headache (OR=1.03, P=0.0217), age of onset (OR=0.92, P<0.0001) and anxiety scores (OR=1.09, P=0.0235). Furthermore, there was no interaction in the age of onset between subgroups. Multivariate linear regression analyses of HIT-6 scores showed that the intensity of headache (β=0.18, P<0.0001) and depression scores (β=0.26, P=0.0009) had independent effects on decreased quality of life.

Conclusion

Our findings suggest that younger age of migraine onset is independently related to migraineurs’ cognitive decline, and migraine accompanying anxiety symptoms significantly related to decreased quality of life in migraineurs.