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Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure. •We study the prediction of MCI to AD conversion using FDG-PET and several classifiers.•We study how the disease stage impacts diagnostic performance.•Results show a decrease in performance with the temporal distance to conversion.•We achieve an accuracy of 85.1% at the time of conversion and 75% 24 months before it.

The outbreak of COVID-19 might produce dramatic psychological effects on individuals’ lives. In this study, we aimed to explore the elements that may reduce the negative effects on mental health of the quarantine period imposed by most governments during this worldwide crisis. We conducted an online survey to evaluate demographic, lifestyle and mental health variables in a sample of 1280 Portuguese individuals (79.8% females) with an average age of 37 years. We observed that factors related to living conditions, maintaining work either online or in the workplace, frequency of exercise and absence of previous psychological or physic disorders are protective features of psychological well-being (anxiety, depression, stress and obsessive-compulsive symptoms). Finally, the individuals previously receiving psychotherapeutic support exhibited better psychological indicators if they did not interrupt the process as a consequence of the outbreak. Our results indicate that the practice of physical exercise, reduced consumption of COVID-19 information and the implementation of remote mental healthcare measures might prevent larger impacts on mental health during the COVID-19 outbreak.

Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks - i.e. Limbic, Dorsal Attention and Somatomotor - synchronized in anti-phase with respect to the rest of the brain. In terms of state-to-state transitions, patients exhibited increased probability of switching to Limbic, Somatomotor and Visual networks, and reduced probability of remaining in states related to the Default Mode network, the Orbitofrontal network and the globally synchronized state. All results revealed medium to large effect sizes. Combined, these findings expose pronounced differences in the temporal expression of resting-state networks in schizophrenia patients, which may relate to the pathophysiology of this disorder. Overall, these results reinforce the utility of dynamical systems theory to extend current knowledge regarding disrupted brain dynamics in psychiatric disorders.

Background The main focus of the non-pharmacological treatment of Gambling Disorder (GD) is the behaviour, cognition and motivation of the patient, addressing the psychological determinants of gambling. Although there is not a gold standard non-pharmacological treatment yet, many studies already had promising results, and the outcomes were even better when pharmacotherapies were combined with psychotherapies. This review intended to synthesise the efficacy of various available non-pharmacological therapies for GD evaluated in randomized controlled trials. Methods A systematic search was conducted in PubMed and in Cochrane Library for randomized controlled trials. Studies were included if participants had GD as their primary diagnosis and excluded if patients had other comorbidities. Results From 320 records identified, 22 studies were included in the critical appraisal. They included a total of 1694 patients, with a mean age of 42.94 years, and a 62.31% of males. Seven trials revealed the efficacy of cognitive behaviour therapy in improving significantly the outcomes. Three studies assessing cognitive therapy showed significant improvements in gambling symptoms, while one study showed improvements in gambling behaviour using exposure therapy. Combined or separate motivational interviewing and imaginal desensitization had significant results in 4 trials. Four other studies also showed efficacy for: couples therapy, node-link mapping therapy, 12-step facilitated and personalized feedback intervention. Physical exercise had promising results but did not reach significance. Conclusion The literature included in this review showed the heterogeneity of available psychotherapies. The majority of studies supported the efficacy of the tested therapies, while some of them, due to limitations such as small sample sizes or inadequate control groups, failed to reach significance.

Major depressive disorder (MDD) is the most common mood disorder and a leading cause of disability worldwide. Trazodone, a triazolopyridine serotonin receptor antagonist and reuptake inhibitor (SARI) antidepressant approved for major depressive disorder (MDD) in adults, has established efficacy that is comparable to other available antidepressants, and is effective for a range of depression symptoms, including insomnia, which is one of the most common and bothersome symptoms of depression. Also, trazodone’s pharmacodynamic properties allow it to avoid the side effects of insomnia, anxiety and sexual dysfunction often associated with selective serotonin reuptake inhibitor antidepressants. In this narrative review, we have summarized recent clinical trials and real-world data on trazodone, including the recently introduced once-daily formulation, which has single dose pharmacokinetic properties that maintain effective blood trazodone levels for 24 h, while avoiding concentration peaks associated with side effects. This, combined with a low incidence of weight gain, and sexual dysfunction, may improve adherence to treatment. The most common adverse effects of trazodone are somnolence, headache, dizziness and xerostomia. It has minimal anticholinergic activity but may be associated infrequently with orthostatic hypotension (especially in patients with cardiovascular disease or older adults), QT interval prolongation, cardiac arrhythmias, and rare episodes of priapism. The low liability for activating side effects, the efficacy on symptoms such as insomnia and psychomotor agitation and the rapid onset of action make it useful for many depressed patients, both in monotherapy at nominal dosages of 150–300 mg/day, and in combination with other antidepressants at lower dosages.

The ability to shift between different behavioral strategies is necessary for appropriate decision-making. Here, we show that chronic stress biases decision-making strategies, affecting the ability of stressed animals to perform actions on the basis of their consequences. Using two different operant tasks, we revealed that, in making choices, rats subjected to chronic stress became insensitive to changes in outcome value and resistant to changes in action-outcome contingency. Furthermore, chronic stress caused opposing structural changes in the associative and sensorimotor corticostriatal circuits underlying these different behavioral strategies, with atrophy of medial prefrontal cortex and the associative striatum and hypertrophy of the sensorimotor striatum. These data suggest that the relative advantage of circuits coursing through sensorimotor striatum observed after chronic stress leads to a bias in behavioral strategies toward habit.

[This corrects the article DOI: 10.3389/fnhum.2022.958706.].[This corrects the article DOI: 10.3389/fnhum.2022.958706.].

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.

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.

Over the past few years, there has been significant interest in the importance of reversible hydro-pumping systems due to their favorable flexibility and economic and environmental characteristics. When designing reversible lines, it is crucial to consider dynamic effects and corresponding extreme pressures that may occur during normal and emergency operating scenarios. This research describes essentially the turbine operation, although various boundary elements are mathematically formulated and presented to provide an understanding of the system complexity. Different numerical approaches are presented, based on the 1D method of characteristics (MOC) for the long hydraulic circuit, the dynamic turbine runner simulation technique for the behavior of the power station in turbine mode and the interaction with the fluid in the penstock, and a CFD model (2D and 3D) to analyze the flow behavior crossing the runner through the velocity fields and pressure contours. Additionally, the simulation results have been validated by experimental tests on different setups characterized by long conveyance systems, consisting of a small scale of pumps as turbines (at IST laboratory) and classical reaction turbines (at LNEC laboratory). Mathematical models, together with an intensive campaign of experiments, allow for the estimation of dynamic effects related to the extreme transient pressures, the fluid-structure interaction with rotational speed variation, and the change in the flow. In some cases, the runaway conditions can cause an overspeed of 2–2.5 of the rated rotational speed (NR) and an overpressure of 40–65% of the rated head (HR), showing significant impacts on the pressure wave propagation along the entire hydraulic circuit. Sensitivity analyses based on systematic numerical simulations of PATs (radial and axial types) and reaction turbines (Francis and Kaplan types) and comparisons with experiments are discussed. These evaluations demonstrate that the full-load rejection scenario can be dangerous for turbomachinery with low specific-speed (ns) values, in particular when associated with long penstocks and fast guide vane (or control valve) closing maneuver.