Investigating racing thoughts via ocular temporal windows: deficits in the control of automatic perceptual processes

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.

An overactive mind: Investigating racing thoughts in ADHD, hypomania and comorbid ADHD and bipolar disorder via verbal fluency tasks

BACKGROUND

Adults with Attention Deficit Hyperactivity Disorder (ADHD) often report mental restlessness akin to racing thoughts found in hypomanic and mixed episodes of bipolar disorder (BD). Past research has suggested that racing thoughts in BD can be tackled via process-oriented verbal fluency measures. In ADHD, it is still unknown whether racing thoughts is due to comorbid BD, and its neuropsychological underpinnings remain to be investigated. To this aim, this study investigates process-oriented verbal fluency measures in adults with ADHD compared to adults with BD in a hypomanic episode, adults with ADHD + BD and healthy controls.

METHODS

Three verbal fluency tasks, i.e., the free, the letter and the semantic conditions, were administered to 37 adults with ADHD, 25 adults with BD in a hypomanic episode, 22 adults with comorbid ADHD + BD (euthymia) and 31 healthy subjects. Word production, clustering, and switching were analysed in verbal fluency tasks.

RESULTS

Adults with ADHD, ADHD + BD and hypomania showed increased switches in the free verbal fluency task, compared to healthy controls, despite equivalent number of words produced. Unlike the ADHD and ADHD + BD groups, phonological clustering measures in the semantic task were increased in the hypomanic group compared to healthy controls.

LIMITATIONS

BD groups were under medication.

CONCLUSIONS

Semantic overactivation, reflected by increased switches in the unconstrained verbal fluency task, appear to be a common mechanism involved in racing thoughts in both ADHD and BD. By contrast, unusual sounds-based associations are specific of hypomanic speech and might contribute to the distinction trait and state racing thoughts.