Recent years have witnessed the emergence of powerful new tools for assaying the brain and a remarkable acceleration of research focused on the interplay of emotion and cognition. This work has begun to yield new insights into fundamental questions about the nature of the mind and important clues about the origins of mental illness. In particular, this research demonstrates that stress, anxiety, and other kinds of emotion can profoundly influence key elements of cognition, including selective attention, working memory, and cognitive control. Often, this influence persists beyond the duration of transient emotional challenges, partially reflecting the slower molecular dynamics of catecholamine and hormonal neurochemistry. In turn, circuits involved in attention, executive control, and working memory contribute to the regulation of emotion. The distinction between the ‘emotional’ and the ‘cognitive’ brain is fuzzy and context-dependent. Indeed, there is compelling evidence that brain territories and psychological processes commonly associated with cognition, such as the dorsolateral prefrontal cortex and working memory, play a central role in emotion. Furthermore, putatively emotional and cognitive regions influence one another via a complex web of connections in ways that jointly contribute to adaptive and maladaptive behavior. This work demonstrates that emotion and cognition are deeply interwoven in the fabric of the brain, suggesting that widely held beliefs about the key constituents of ‘the emotional brain’ and ‘the cognitive brain’ are fundamentally flawed. We conclude by outlining several strategies for enhancing future research. Developing a deeper understanding of the emotional-cognitive brain is important, not just for understanding the mind but also for elucidating the root causes of its disorders.

Women show increased predisposition for certain psychiatric disorders, such as depression, that are associated with disturbances in the integration of emotion and cognition. While this suggests that sex hormones need to be considered as modulating factors in the regulation of emotion, we still lack a sound understanding of how the menstrual cycle impacts emotional states and cognitive function. Though signals for the influence of the menstrual cycle on the integration of emotion and cognition have appeared as secondary findings in numerous behavioral and neuroimaging studies, this has only very rarely been the primary research goal. This review summarizes evidence: (1) that the menstrual cycle modulates the integration of emotional and cognitive processing on a behavioral level, and (2) that this change in behavior can be associated with functional, molecular and structural changes in the brain during a specific menstrual cycle phase. The growing evidence for menstrual cycle-specific differences suggests a modulating role for sex hormones on the neural networks supporting the integration of emotional and cognitive information. It will further be discussed what methodological aspects need to be considered to capture the role of the menstrual cycle in the emotion-cognition interplay more systematically.

In light of the need for objective mechanism-based diagnostic tools, the current research describes a novel diagnostic support system aimed to differentiate between anxiety and depression disorders in a clinical sample. Eighty-six psychiatric patients with clinical anxiety and/or depression were recruited from a public hospital and assigned to one of the experimental groups: Depression, Anxiety, or Mixed. The control group included 25 participants with no psychiatric diagnosis. Participants performed a battery of six cognitive-behavioral tasks assessing biases of attention, expectancies, memory, interpretation and executive functions. Data were analyzed with a machine-learning (ML) random forest-based algorithm and cross-validation techniques. The model assigned participants to clinical groups based solely on their aggregated cognitive performance. By detecting each group's unique performance pattern and the specific measures contributing to the prediction, the ML algorithm predicted diagnosis classification in two models: (I) anxiety/depression/mixed vs. control (76.81% specificity, 69.66% sensitivity), and (II) anxiety group vs. depression group (80.50% and 66.46% success rates in classifying anxiety and depression, respectively). The findings demonstrate that the cognitive battery can be utilized as a support system for psychiatric diagnosis alongside the clinical interview. This implicit tool, which is not based on self-report, is expected to enable the clinician to achieve increased diagnostic specificity and precision. Further, this tool may increase the confidence of both clinician and patient in the diagnosis by equipping them with an objective assessment tool. Finally, the battery provides a profile of biased cognitions that characterizes the patient, which in turn enables more fine-tuned, individually-tailored therapy.

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

Emotion regulation plays a central role in empathy. Only by successfully regulating our own emotions can we reliably use them in order to interpret the content and valence of others’ emotions correctly. In an functional magnetic resonance imaging (fMRI)-based experiment, we show that regulating one’s emotion via reappraisal modulated biased emotional intensity ratings following an empathy for pain manipulation. Task-based analysis revealed increased activity in the right inferior frontal gyrus (IFG) when painful emotions were regulated using reappraisal, whereas empathic feelings that were not regulated resulted in increased activity bilaterally in the precuneus, supramarginal gyrus and middle frontal gyrus (MFG), as well as the right parahippocampal gyrus. Functional connectivity analysis indicated that the right IFG plays a role in the regulation of empathy for pain, through its connections with regions in the empathy for pain network. Furthermore, these connections were further modulated as a function of the type of regulation used: in sum, our results suggest that accurate empathic judgment (i.e. empathy that is unbiased) relies on a complex interaction between neural regions involved in emotion regulation and regions associated with empathy for pain. Thus, demonstrating the importance of emotion regulation in the formulation of complex social systems and sheds light on the intricate network implicated in this complex process.