Accumulating evidence points toward a very high prevalence of prolonged neurological symptoms among coronavirus disease 2019 (COVID-19) survivors. To date, there are no solidified criteria for ...‘long-COVID’ diagnosis. Nevertheless, ‘long-COVID’ is conceptualized as a multi-organ disorder with a wide spectrum of clinical manifestations that may be indicative of underlying pulmonary, cardiovascular, endocrine, hematologic, renal, gastrointestinal, dermatologic, immunological, psychiatric, or neurological disease. Involvement of the central or peripheral nervous system is noted in more than one-third of patients with antecedent severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, while an approximately threefold higher incidence of neurological symptoms is recorded in observational studies including patient-reported data. The most frequent neurological manifestations of ‘long-COVID’ encompass fatigue; ‘brain fog’; headache; cognitive impairment; sleep, mood, smell, or taste disorders; myalgias; sensorimotor deficits; and dysautonomia. Although very limited evidence exists to date on the pathophysiological mechanisms implicated in the manifestation of ‘long-COVID’, neuroinflammatory and oxidative stress processes are thought to prevail in propagating neurological ‘long-COVID’ sequelae. In this narrative review, we sought to present a comprehensive overview of our current understanding of clinical features, risk factors, and pathophysiological processes of neurological ‘long-COVID’ sequelae. Moreover, we propose diagnostic and therapeutic algorithms that may aid in the prompt recognition and management of underlying causes of neurological symptoms that persist beyond the resolution of acute COVID-19. Furthermore, as causal treatments for ‘long-COVID’ are currently unavailable, we propose therapeutic approaches for symptom-oriented management of neurological ‘long-COVID’ symptoms. In addition, we emphasize that collaborative research initiatives are urgently needed to expedite the development of preventive and therapeutic strategies for neurological ‘long-COVID’ sequelae.
COVID-19 pandemic has undoubtedly disrupted the well-established, traditional structure of medical education. Τhe new limitations of physical presence have accelerated the development of an online ...learning environment, comprising both of asynchronous and synchronous distance education, and the introduction of novel ways of student assessment. At the same time, this prolonged crisis had serious implications on the lives of medical students including their psychological well-being and the impact on their academic trajectories. The new reality has, on many occasions, triggered the 'acting up' of medical students as frontline healthcare staff, which has been perceived by many of them as a positive learning and contributing experience, and has led to a variety of responses from the educational institutions. All things considered, the urgency for rapid and novel adaptations to the new circumstances has functioned as a springboard for remarkable innovations in medical education,including the promotion of a more "evidence-based" approach.
We investigated the potential of machine learning for diagnostic classification in late-life major depression based on an advanced whole brain white matter segmentation framework. Twenty-six ...late-life depression and 12 never depressed individuals aged > 55 years, matched for age, MMSE, and education underwent brain diffusion tensor imaging and a multi-contrast, multi-atlas segmentation in MRIcloud. Fractional anisotropy volume, mean fractional anisotropy, trace, axial and radial diffusivity (RD) extracted from 146 white matter parcels for each subject were used to train and test the AdaBoost classifier using stratified 12-fold cross validation. Performance was evaluated using various measures. The statistical power of the classifier was assessed using label permutation test. Statistical analysis did not yield significant differences in DTI measures between the groups. The classifier achieved a balanced accuracy of 71% and an Area Under the Receiver Operator Characteristic Curve (ROC-AUC) of 0.81 by trace, and a balanced accuracy of 70% and a ROC-AUC of 0.80 by RD, in limbic, cortico-basal ganglia-thalamo-cortical loop, brainstem, external and internal capsules, callosal and cerebellar structures. Both indices shared important structures for classification, while fornix was the most important structure for classification by both indices. The classifier proved statistically significant, as trace and RD ROC-AUC scores after permutation were lower than those obtained with the actual data (P = 0.022 and P = 0.024, respectively). Similar results were obtained with the Gradient Boosting classifier, whereas the RBF-kernel Support Vector Machine with k-best feature selection did not exceed the chance level. Finally, AdaBoost significantly predicted the class using all features together. Limitations are discussed. The results encourage further investigation of the implemented methods for computer aided diagnostics and anatomically informed therapeutics.
Paradoxical Reasoning: An fMRI Study Belekou, Antigoni; Papageorgiou, Charalabos; Karavasilis, Efstratios ...
Frontiers in psychology,
05/2022, Letnik:
13
Journal Article
Recenzirano
Odprti dostop
Paradoxes are a special form of reasoning leading to absurd inferences in contrast to logical reasoning that is used to reach valid conclusions. A functional MRI (fMRI) study was conducted to ...investigate the neural substrates of paradoxical and deductive reasoning. Twenty-four healthy participants were scanned using fMRI, while they engaged in reasoning tasks based on arguments, which were either Zeno's like paradoxes (paradoxical reasoning) or Aristotelian arguments (deductive reasoning). Clusters of significant activation for paradoxical reasoning were located in bilateral inferior frontal and middle temporal gyrus. Clusters of significant activation for deductive reasoning were located in bilateral superior and inferior parietal lobe, precuneus, and inferior frontal gyrus. These results confirmed that different brain activation patterns are engaged for paradoxical vs. deductive reasoning providing a basis for future studies on human physiological as well as pathological reasoning.
•Parkinson Disease (PD) and Schizophrenia (SCZ) share cortical dopamine dysfunction.•SCZ and PD were compared in a speeded decision processing task using fMRI.•Normal performance in PD contrasted ...with performance deviance in SCZ.•Cortical hypo-activity in SCZ contrasted with normal task activation in PD.•The results do not favour common cognitive/cortical dysfunction PD and SCZ.
This study examined whether Parkinson's disease (PD11Parkinson Disease) and schizophrenia (SCZ22Schizophrenia) share a hypo dopaminergic dysfunction of the prefrontal cortex leading to cognitive impairments in decision processing. 24 medicated PD patients and 28 matched controls performed the Eriksen flanker two-choice reaction time (RT33Reaction Time) task while brain activity was measured throughout, using functional Magnetic Resonance Imaging (fMRI44functional Magnetic Resonance Imaging). Results were directly compared to those of 30 SCZ patients and 30 matched controls. Significant differences between SCZ and PD were found, through directly comparing the z-score deviations from healthy controls across all behavioral measures, where only SCZ patients showed deviances from controls. Similarly a direct comparison of z-score activation deviations from controls indicated significant differences in prefrontal and cingulate cortical activation between SCZ and PD, where only SCZ patients showed hypo-activation of these areas compared to controls. The hypo-activation of the dorsolateral prefrontal cortex was related to larger RT variability (ex-Gaussian tau) in SCZ but not PD patients. Overall, the concluding evidence does not support a shared neural substrate of cognitive dysfunction, since the deficit in speeded decision processing and the related cortical hypo-activation observed in SCZ were absent in PD.
This study investigated patient- and caregiver-related predictors of expressed emotion (EE) toward individuals with schizophrenia in families and halfway houses and yet understudied differential ...effects across settings.
We included 40 individuals with schizophrenia living with their families ("outpatients") and 40 "inpatients" in halfway houses and recorded the EE of 56 parents or 22 psychiatric nurses, respectively, through Five Minutes Speech Sample. Each outpatient was rated by one to two parents; each inpatient was rated by two to five nurses. As EE ratings had a multilevel structure, EE predictors were investigated in backward stepwise generalized linear mixed models using the "buildmer" R package. We first fitted models including either caregiver- or patient-related predictors in each setting and finally included both types of predictors. Setting-specific patient-related effects were investigated in interaction analyses. Adjustment for multiple tests identified the most robust associations.
In multivariate models including either caregiver- or patient-related predictors, nurses' higher age, shorter work experience and lower inpatients' negative symptoms robustly predicted higher emotional overinvolvement (EOI). In the final models including both types of predictors, nurses robustly displayed lower EOI (i.e., reduced concern and disengagement) toward inpatients with higher negative symptoms. Several other features were nominally associated with criticism and EOI in each setting. However, no feature robustly predicted criticism in inpatients and criticism/EOI in outpatients after adjustment for multiple tests. In interaction analyses, higher negative symptoms differentially predicted lower EOI in nurses only.
Our findings suggest setting-specific pathogenetic pathways of EOI and might help customize psychoeducational interventions to staff in halfway houses.
Wearable technologies and digital phenotyping foster unique opportunities for designing novel intelligent electronic services that can address various well-being issues in patients with mental ...disorders (i.e., schizophrenia and bipolar disorder), thus having the potential to revolutionize psychiatry and its clinical practice. In this paper, we present e-Prevention, an innovative integrated system for medical support that facilitates effective monitoring and relapse prevention in patients with mental disorders. The technologies offered through e-Prevention include: (i) long-term continuous recording of biometric and behavioral indices through a smartwatch; (ii) video recordings of patients while being interviewed by a clinician, using a tablet; (iii) automatic and systematic storage of these data in a dedicated Cloud server and; (iv) the ability of relapse detection and prediction. This paper focuses on the description of the e-Prevention system and the methodologies developed for the identification of feature representations that correlate with and can predict psychopathology and relapses in patients with mental disorders. Specifically, we tackle the problem of relapse detection and prediction using Machine and Deep Learning techniques on all collected data. The results are promising, indicating that such predictions could be made and leading eventually to the prediction of psychopathology and the prevention of relapses.
Abstract
Findings of genetic overlap between Schizophrenia, Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) contributed to a renewed conceptualization of these ...disorders as laying on a continuum based on aetiological, pathophysiological and neurodevelopmental features. Given that cognitive impairments are core to their pathophysiology, we compared patients with schizophrenia, ADHD, ASD, and controls on ocular-motor and manual-motor tasks, challenging crucial cognitive processes. Group comparisons revealed inhibition deficits common to all disorders, increased intra-subject variability in schizophrenia and, to a lesser extent, ADHD as well as slowed processing in schizophrenia. Patterns of deviancies from controls exhibited strong correlations, along with differences that posited schizophrenia as the most impaired group, followed by ASD and ADHD. While vector correlations point towards a common neurodevelopmental continuum of impairment, vector levels suggest differences in the severity of such impairment. These findings argue towards a dimensional approach to Neurodevelopmental Disorders’ pathophysiological mechanisms.
In recent years, psychiatric research has focused on the evaluation and implementation of biomarkers in the clinical praxis. Oculomotor function deviances are among the most consistent and replicable ...cognitive deficits in schizophrenia and have been suggested as viable candidates for biomarkers. In this narrative review, we focus on oculomotor function in first-episode psychosis, recent onset schizophrenia as well as individuals at high risk for developing psychosis. We critically discuss the evidence for the possible utilization of oculomotor function measures as diagnostic, susceptibility, predictive, monitoring, and prognostic biomarkers for these conditions. Based on the current state of research we conclude that there are not sufficient data to unequivocally support the use of oculomotor function measures as biomarkers in schizophrenia.
Interleukin-1 beta (IL1β) is primarily produced by monocytes in the periphery and the brain. Yet, IL1β protein levels have to date been investigated in major depressive disorder (MDD) and ...antidepressant response using either plasma or serum assays although with contradictory results, while mononuclear cell assays are lacking despite their extensive use in other contexts. In this pilot study, we comparatively assessed IL1β in mononuclear lysates and plasma in depressed MDD patients over treatment and healthy controls (HC). We recruited 31 consecutive adult MDD inpatients and 25 HC matched on age, sex, and BMI. Twenty-six patients completed an 8-week follow-up under treatment. IL1β was measured in both lysates and plasma in patients at baseline (T0) and at study end (T1) as well as in HC. We calculated ΔIL1β(%) for both lysates and plasma as IL1β percent changes from T0 to T1. Seventeen patients (65.4% of completers) were responders at T1 and had lower baseline BMI than non-responders (
= 0.029). Baseline IL1β from either plasma or lysates could not efficiently discriminate between depressed patients and HC, or between responders and non-responders. However, the two response groups displayed contrasting IL1β trajectories in lysates but not in plasma assays (response group by time interactions,
= 0.005 and 0.96, respectively). ΔIL1β(%) in lysates predicted response (
= 0.025, AUC = 0.81; accuracy = 84.6%) outperforming ΔIL1β(%) in plasma (
= 0.77, AUC=0.52) and was robust to adjusting for BMI. In conclusion, ΔIL1β(%) in mononuclear lysates may be a longitudinal biomarker of antidepressant response, potentially helpful in avoiding untimely switching of antidepressants, thereby warranting further investigation.
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