Autobiographical Memory and Episodic Specificity Across Different Affective States in Bipolar Disorder

The cognitive neuroscience of self-awareness: Current framework, clinical implications, and future research directions

Self-awareness, the ability to take oneself as the object of awareness, has been an enigma for our species, with different answers to this question being provided by religion, philosophy, and, more recently, science. The current review aims to discuss the neurocognitive mechanisms underlying self-awareness. The multidimensional nature of self-awareness will be explored, suggesting how it can be thought of as an emergent property observed in different cognitive complexity levels, within a predictive coding approach. A presentation of alterations of self-awareness in neuropsychiatric conditions will ground a discussion on alternative frameworks to understand this phenomenon, in health and psychopathology, with future research directions being indicated to fill current gaps in the literature. This article is categorized under: Philosophy > Consciousness Psychology > Brain Function and Dysfunction Neuroscience > Cognition.

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.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.