What is considered normal determines clinical practice in medicine and has implications at an individual level, doctor-patient relationship and health care policies. With the increase in medical information and technical abilities it is urgent to have a clear concept of normality in medicine so that crucial discussions can be held with unequivocal terms.The different meanings for normality were analyzed throughout the literature and grouped according to their relevance in the academic community in models, namely the Biostatistical Theory (BST), Health, Ideal, (...) Process and Biological advantage. The BST is the most established naturalistic approach, however normal variability can arguably constitute a problem. Health is similar and raises the question of setting the boundaries of pathology. Normality as an Ideal is an useful tool but is naturally unrealistic. As a Process it is comprehensible but is hard to frame for practical purposes. If considered as a Biological Advantage, seems intuitive but abnormality should tend to disappear.After, three examples were presented to discuss these models. They were Anemia, Psychiatric diseases and Psychopathy. In the case of Anemia the BST was applied and the arbitrary boundaries but with social impact were exposed. Psychiatric diseases was discussed under the process of self-organization and non-suffering ideal. With Psychopathy the boundaries of biological advantage are questioned.This review appeals to the importance of redesigning of the concept of normality in medicine according to current times and stresses the importance of integrating concepts such as variability and autonomy. (shrink)

The past two decades have seen an explosion in the methods and directions of neuroscience research. Along with many others, complexity research has rapidly gained traction as both an independent research field and a valuable subdiscipline in computational neuroscience. In the past decade alone, several studies have suggested that psychiatric disorders affect the spatiotemporal complexity of both global and region-specific brain activity (Liu et al., 2013; Adhikari et al., 2017; Li et al., 2018). However, many of these studies have not (...) accounted for the distributed nature of cognition in either the global or regional complexity estimates, which may lead to erroneous interpretations of both global and region-specific entropy estimates. To alleviate this concern, we propose a novel method for estimating complexity. This method relies upon projecting dynamic functional connectivity into a low-dimensional space which captures the distributed nature of brain activity. Dimension-specific entropy may be estimated within this space, which in turn allows for a rapid estimate of global signal complexity. Testing this method on a recently acquired obsessive-compulsive disorder dataset reveals substantial increases in the complexity of both global and dimension-specific activity versus healthy controls, suggesting that obsessive-compulsive patients may experience increased disorder in cognition. To probe the potential causes of this alteration, we estimate subject-level effective connectivity via a Hopf oscillator-based model dynamic model, the results of which suggest that obsessive-compulsive patients may experience abnormally high connectivity across a broad network in the cortex. These findings are broadly in line with results from previous studies, suggesting that this method is both robust and sensitive to group-level complexity alterations. (shrink)