Editor’s Choice Articles

Multiple lines of evidence are known to confirm the pro-inflammatory state of some patients with schizophrenia and the involvement of inflammatory mechanisms in the pathogenesis of psychosis. The concentration of peripheral biomarkers is associated with the severity of inflammation and can be used for patient stratification. Here, we analyzed changes in serum concentrations of cytokines (IL-1β, IL-2, IL-4, IL-6, IL-10, IL-21, APRIL, BAFF, PBEF/Visfatin, IFN-α, and TNF-α) and growth/neurotrophic factors (GM-CSF, NRG1-β1, NGF-β, and GDNF) in patients with schizophrenia in an exacerbation phase. IL-1β, IL-2, IL-4, IL-6, BAFF, IFN-α, GM-CSF, NRG1-β1, and GDNF increased but TNF-α and NGF-β decreased in schizophrenia compared to healthy individuals. Subgroup analysis revealed the effect of sex, prevalent symptoms, and type of antipsychotic therapy on biomarker levels. Females, patients with predominantly negative symptoms, and those taking atypical antipsychotics had a more pro-inflammatory phenotype. Using cluster analysis, we classified participants into “high” and “low inflammation” subgroups. However, no differences were found in the clinical data of patients in these subgroups. Nevertheless, more patients (17% to 25.5%) than healthy donors (8.6% to 14.3%) had evidence of a pro-inflammatory condition depending on the clustering approach used. Such patients may benefit from personalized anti-inflammatory therapy. Full article

Epilepsy is a central nervous system disorder involving spontaneous and recurring seizures that affects 50 million individuals globally. Because approximately one-third of patients with epilepsy do not respond to drug therapy, the development of new therapeutic strategies against epilepsy could be beneficial. Oxidative stress and mitochondrial dysfunction are frequently observed in epilepsy. Additionally, neuroinflammation is increasingly understood to contribute to the pathogenesis of epilepsy. Mitochondrial dysfunction is also recognized for its contributions to neuronal excitability and apoptosis, which can lead to neuronal loss in epilepsy. This review focuses on the roles of oxidative damage, mitochondrial dysfunction, NAPDH oxidase, the blood–brain barrier, excitotoxicity, and neuroinflammation in the development of epilepsy. We also review the therapies used to treat epilepsy and prevent seizures, including anti-seizure medications, anti-epileptic drugs, anti-inflammatory therapies, and antioxidant therapies. In addition, we review the use of neuromodulation and surgery in the treatment of epilepsy. Finally, we present the role of dietary and nutritional strategies in the management of epilepsy, including the ketogenic diet and the intake of vitamins, polyphenols, and flavonoids. By reviewing available interventions and research on the pathophysiology of epilepsy, this review points to areas of further development for therapies that can manage epilepsy. Full article

Stroke is the fourth leading cause of mortality and is estimated to be one of the major reasons for long-lasting disability worldwide. There are limited studies that describe the application of physical therapy interventions to prevent disabilities in stroke survivors and promote recovery after a stroke. In this review, we have described a wide range of interventions based on impairments, activity limitations, and goals in recovery during different stages of a stroke. This article mainly focuses on stroke rehabilitation tactics, including those for sensory function impairments, motor learning programs, hemianopia and unilateral neglect, flexibility and joint integrity, strength training, hypertonicity, postural control, and gait training. We conclude that, aside from medicine, stroke rehabilitation must address specific functional limitations to allow for group activities and superior use of a hemiparetic extremity. Medical doctors are often surprised by the variety of physiotherapeutic techniques available; they are unfamiliar with the approaches of researchers such as Bobath, Coulter, and Brunnstrom, among others, as well as the scientific reasoning behind these techniques. Full article

Biofeedback training is a method commonly used in various fields of life, for example, in medicine, sports training or business. In recent studies, it has been shown that biofeedback, and neurofeedback, can affect the performance of professional athletes. Training based on the neurofeedback method includes exercising the brain waves. The aim of the article is to evaluate the influence of neurofeedback training on the physical fitness of professional athletes representing various sports disciplines, such as judo, volleyball and soccer. Based on 10 scientific papers from various sources, including PubMed, the latest research on neurofeedback and its impact on athletes has been reviewed. On the basis of the literature review from 2012 to 2022 on the neurofeedback method in sports training, it can be stated that this type of practice has a significant impact on physical fitness and sports performance. This review comprised 10 research studies with 491 participants in the neurofeedback groups, and 62 participants in the control group. Two reviewers independently extracted data and evaluated the quality of the studies utilising the PEDro scale. Properly planned and conducted neurofeedback training affects stimulation and improvement of many variables (reducing stress levels, increasing the ability to self-control physiological factors, enhancing behavioural efficiency and meliorating the speed of reaction to a stimulus). Full article

Objective: The purpose of this study was to investigate the cortical activity and digit classification performance during tactile imagery (TI) of a vibratory stimulus at the index, middle, and thumb digits within the left hand in healthy individuals. Furthermore, the cortical activities and classification performance of the compound TI were compared with similar compound motor imagery (MI) with the same digits as TI in the same subjects. Methods: Twelve healthy right-handed adults with no history of upper limb injury, musculoskeletal condition, or neurological disorder participated in the study. The study evaluated the event-related desynchronization (ERD) response and brain–computer interface (BCI) classification performance on discriminating between the digits in the left-hand during the imagery of vibrotactile stimuli to either the index, middle, or thumb finger pads for TI and while performing a motor activity with the same digits for MI. A supervised machine learning technique was applied to discriminate between the digits within the same given limb for both imagery conditions. Results: Both TI and MI exhibited similar patterns of ERD in the alpha and beta bands at the index, middle, and thumb digits within the left hand. While TI had significantly lower ERD for all three digits in both bands, the classification performance of TI-based BCI (77.74 ± 6.98%) was found to be similar to the MI-based BCI (78.36 ± 5.38%). Conclusions: The results of this study suggest that compound tactile imagery can be a viable alternative to MI for BCI classification. The study contributes to the growing body of evidence supporting the use of TI in BCI applications, and future research can build on this work to explore the potential of TI-based BCI for motor rehabilitation and the control of external devices. Full article

Brain tumor classification is crucial for medical evaluation in computer-assisted diagnostics (CAD). However, manual diagnosis of brain tumors from magnetic resonance imaging (MRI) can be time-consuming and complex, leading to inaccurate detection and classification. This is mainly because brain tumor identification is a complex procedure that relies on different modules. The advancements in Deep Learning (DL) have assisted in the automated process of medical images and diagnostics for various medical conditions, which benefits the health sector. Convolutional Neural Network (CNN) is one of the most prominent DL methods for visual learning and image classification tasks. This study presents a novel CNN algorithm to classify the brain tumor types of glioma, meningioma, and pituitary. The algorithm was tested on benchmarked data and compared with the existing pre-trained VGG16, VGG19, ResNet50, MobileNetV2, and InceptionV3 algorithms reported in the literature. The experimental results have indicated a high classification accuracy of 98.04%, precision, recall, and f1-score success rate of 98%, respectively. The classification results proved that the most common kinds of brain tumors could be categorized with a high level of accuracy. The presented algorithm has good generalization capability and execution speed that can be helpful in the field of medicine to assist doctors in making prompt and accurate decisions associated with brain tumor diagnosis. Full article

Objective: Sufferers of tinnitus, especially of the prolonged type, frequently suffer from comorbid depression and anxiety. From the perspective of the network model, this comorbidity is thought to be an interacting system of these two symptoms. In our study, we conducted a network analysis of depression and anxiety comorbidity in tinnitus sufferers, aiming to identify the central and bridge symptoms and make informed suggestions for clinical interventions and psychotherapy. Method: A total of 566 tinnitus sufferers were enrolled in our study. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7) were selected to evaluate depression and anxiety symptoms, respectively, followed by network analysis to construct the interacting networks. Results: The findings identified six edges of strongest regularized partial correlations in this network. Of these, three were depression symptoms and three were anxiety symptoms. The anxiety symptoms “Unable to control worry” and “Relaxation difficulty” and the depression symptom “Feeling depressed or hopeless” had the highest expected influence centrality. The analysis results also revealed three bridge symptoms: “Afraid something awful might happen”, “Feeling of worthlessness”, and “Trouble concentrating”. As for “Suicidal ideation”, the direct relations between this symptom and “Afraid something awful might happen” and “Feeling depressed or hopeless” were the strongest. Conclusions: The central and bridge symptoms of the interacting network of depression and anxiety symptoms in tinnitus sufferers can be considered a significant transdiagnostic intervention target for the management of this comorbidity. In particular, clinical prevention and psychotherapy should be implemented, targeting the symptoms that have the strongest associations with suicidal ideation. Full article

Gliomas are primary malignant brain tumors. These tumors seem to be more and more frequent, not only because of a true increase in their incidence, but also due to the increase in life expectancy of the general population. Among gliomas, malignant gliomas and more specifically glioblastomas (GBM) are a challenge in their diagnosis and treatment. There are few effective therapies for these tumors, and patients with GBM fare poorly, even after aggressive surgery, chemotherapy, and radiation. Over the last decade, it is now appreciated that these tumors are composed of numerous distinct tumoral and non-tumoral cell populations, which could each influence the overall tumor biology and response to therapies. Monocytes have been proved to actively participate in tumor growth, giving rise to the support of tumor-associated macrophages (TAMs). In GBM, TAMs represent up to one half of the tumor mass cells, including both infiltrating macrophages and resident brain microglia. Infiltrating macrophages/monocytes constituted ~ 85% of the total TAM population, they have immune functions, and they can release a wide array of growth factors and cytokines in response to those factors produced by tumor and non-tumor cells from the tumor microenvironment (TME). A brief review of the literature shows that this cell population has been increasingly studied in GBM TME to understand its role in tumor progression and therapeutic resistance. Through the knowledge of its biology and protumoral function, the development of therapeutic strategies that employ their recruitment as well as the modulation of their immunological phenotype, and even the eradication of the cell population, can be harnessed for therapeutic benefit. This revision aims to summarize GBM TME and localization in tumor niches with special focus on TAM population, its origin and functions in tumor progression and resistance to conventional and experimental GBM treatments. Moreover, recent advances on the development of TAM cell targeting and new cellular therapeutic strategies based on monocyte/macrophages recruitment to eradicate GBM are discussed as complementary therapeutics. Full article

The literature has long established the association between aging and frailty, with emerging evidence pointing to a relationship between frailty and SARS-CoV-2 contagion. The possible neurological consequences of SARS-CoV-2 infection, associated with physical and cognitive frailty, could lead to a worsening of Parkinson’s disease (PD) in infected patients or—more rarely—to an increase in the Parkinsonian symptomatology. A possible link between those clinical pictures could be identified in vitamin D deficiency, while the whole process would appear to be associated with alterations in the microbiota–intestine–brain axis that fall within the α-Synuclein Origin site and Connectome (SOC) model, and allow for the identification of a body-first PD and a brain-first PD. The model of care for this condition must consider intrinsic and extrinsic variables so that care by a multidisciplinary team can be successfully predicted. A multidimensional screening protocol specifically designed to identify people at risk or in the early stages of the disease should begin with the investigation of indices of frailty and microbiota–intestine–brain axis alterations, with a new focus on cases of hypovitaminosis D. Full article

The main histopathological hallmarks of Parkinson’s disease (PD) are the degeneration of the dopaminergic neurons of the substantia nigra pars compacta and the loss of neuromelanin as a consequence of decreased dopamine synthesis. The destruction of the striatal dopaminergic pathway and blocking of striatal dopamine receptors cause motor deficits in humans and experimental animal models induced by some environmental agents. In addition, neuropsychiatric symptoms such as mood and anxiety disorders, hallucinations, psychosis, cognitive impairment, and dementia are common in PD. These alterations may precede the appearance of motor symptoms and are correlated with neurochemical and structural changes in the brain. This paper reviews the most crucial pathophysiology of neuropsychiatric alterations in PD. It is worth noting that PD patients have global task learning deficits, and cognitive functions are compromised in a way is associated with hypoactivation within the striatum, anterior cingulate cortex, and inferior frontal sulcus regions. An appropriate and extensive neuropsychological screening battery in PD must accurately assess at least five cognitive domains with some tests for each cognitive domain. This neuropsychological screening should consider the pathophysiological and clinical heterogeneity of cognitive dysfunction in PD. Full article

Background. Finding the associations between schizophrenia symptoms and the biomarkers of inflammation, oxidative stress and the kynurenine pathway may lead to the individualization of treatment and increase its effectiveness. Methods. The study group included 82 schizophrenia inpatients. The Positive and Negative Symptoms Scale (PANSS), the Brief Assessment of Cognition in Schizophrenia (BACS) and the Calgary Depression in Schizophrenia Scale were used for symptom evaluation. Biochemical analyses included oxidative stress parameters and brain-derived neurotrophic factor (BDNF). Results. Linear models revealed the following: (1) malondiadehyde (MDA), N-formylkynurenine (N-formKYN), advanced oxidation protein products (AOPP), advanced glycation end-products of proteins (AGE) and total oxidative status (TOS) levels are related to the PANSS-total score; (2) MDA, reduced glutathione (GSH) and BDNF levels are related to the PANSS-negative score; (3) TOS and kynurenine (KYN) levels are related to the PANSS-positive score; (4) levels of total antioxidant status (TAS) and AOPP along with the CDSS score are related to the BACS-total score; (5) TAS and N-formKYN levels are related to the BACS-working memory score. Conclusions. Oxidative stress biomarkers may be associated with the severity of schizophrenia symptoms in positive, negative and cognitive dimensions. The identification of biochemical markers associated with the specific symptom clusters may increase the understanding of biochemical profiles in schizophrenia patients. Full article

Objective: The coronavirus belongs to the family of Coronaviridae, which are not branched single-stranded RNA viruses. COVID-19 creates respiratory problems and infections ranging from mild to severe. The virus features mechanisms that serve to delay the cellular immune response. The host’s response is responsible for the pathological process that leads to tissue destruction. Temporomandibular disorders are manifested by painful jaw musculature and jaw joint areas, clicks, or creaks when opening or closing the mouth. All these symptoms can be disabling and occur during chewing and when the patient yawns or even speaks. The pandemic situation has exacerbated anxieties and amplified the vulnerability of individuals. Therefore, from this mechanism, how the COVID-19 pandemic may have increased the incidence of temporomandibular disorders is perceived. The purpose of this review is to evaluate whether COVID-19-related anxiety has caused an increase in temporomandibular dysfunction symptoms in adults to children. Methods: PubMed, Web of Science, Lilacs, and Scopus were systematically searched, until 30 July 2022, to identify studies presenting: the connection between COVID-19 with temporomandibular disorders. Results: From 198 papers, 4 studies were included. Literature studies have shown that the state of uncertainty and anxiety has led to an increase in the incidence of this type of disorder, although not all studies agree. Seventy-three studies were identified after viewing all four search engines; at the end of the screening phase, only four were considered that met the PECO, the planned inclusion, and the exclusion criteria. All studies showed a statistically significant correlation between temporomandibular disorders and COVID-19 with a p < 0.05. Conclusions: All studies agreed that there is an association between COVID-19 and increased incidence of temporomandibular disorders. Full article

In the recent years, growing attention has been paid to the use of camouflaging strategies by adult populations suffering from autism spectrum disorder (ASD) with milder manifestations and without intellectual impairment, which may lead to a delay in diagnosis or even a misdiagnosis. In fact, high-functioning ASD individuals were reported to be more aware of their communication difficulties and were more likely make considerable efforts to adjust their behavior to conventional rules of non-autistic individuals, learning to imitate other non-ASD individuals. Moreover, females reported a higher frequency of camouflaging strategies, suggesting a role of camouflaging in the gender gap of the ASD diagnosis. Although camouflaging strategies can sometimes grant a better level of adjustment, even resulting in a hyper-adaptive behavior, they are also often correlated with negative mental health consequences due to the long-term stress associated with continuous attempts to adapt in day-to-day life. In this framework, the aim of the present work was to review the available studies that assessed the presence and correlates of camouflaging strategies in individuals with ASD. Although the literature available on the topic is still scarce, some interesting correlations between camouflaging and anxious and depressive symptoms, as well as suicidality, were highlighted. In particular, the controversial and sometime opposite thoughts and results about camouflaging may be clarified and integrated in light of a dimensional approach to psychopathology. Full article

The second language acquisition (SLA) field has recently seen heightened interest in the study and application of positive psychology (PP). Emotion regulation is one of the concepts that has been stressed in PP. Several studies in PP have delved into how controlling one’s emotions improves second language learning/teaching. One of the concepts that has slipped the minds of researchers in the field is altruistic teaching. Unlike egocentric acts, altruistic teaching acts are performed to improve others’ well-being. Despite their importance in causing positive emotional effects, no study has investigated the impact of altruistic teaching acts on learners’ emotion regulation. To bridge this gap, the present study sought to investigate the effect of learners’ altruistic teaching on their emotion regulation. The study followed a sequential explanatory comparison group pre-test–post-test design. One hundred forty-one English as a Foreign Language (EFL) learners were recruited for this intervention study and were divided into experimental and control groups. Learners in the experimental group performed altruistic teaching by teaching their peers how to write essays in English, whereas learners in the control group did group work tasks on English essay writing. The results of independent-sample t-tests and repeated-measures ANOVA showed that altruistic teaching significantly impacts EFL learners’ emotion regulation. The results of qualitative data pointed to five themes, including enjoyment, self-esteem, bonding, devotion, and progress. Overall, the results suggested that altruistic teaching impacts learners’ emotion regulation by enhancing their enjoyment, self-esteem, bonding, devotion, and progress. The paper has theoretical and pedagogical implications for SLA research and practice. Full article

This article reviews the current status of research on the relationship between attachment and trauma in developmental psychopathology. Beginning with a review of the major issues and the state-of-the-art in relation to current thinking in the field of attachment about the impact of trauma and the inter-generational transmission of trauma, the review then considers recent neurobiological work on mentalizing and trauma and suggests areas of new development and implications for clinical practice. Full article

Schizophrenia is regarded as a neurodevelopmental disorder with its course progressing throughout life. However, the aetiology and development of schizophrenia are still under investigation. Several data suggest that the dysfunction of epigenetic mechanisms is known to be involved in the pathomechanism of this mental disorder. The present article revised the epigenetic background of schizophrenia based on the data available in online databases (PubMed, Scopus). This paper focused on the role of epigenetic regulation, such as DNA methylation, histone modifications, and interference of non-coding RNAs, in schizophrenia development. The article also reviewed the available data related to epigenetic regulation that may modify the severity of the disease as a possible target for schizophrenia pharmacotherapy. Moreover, the effects of antipsychotics on epigenetic malfunction in schizophrenia are discussed based on preclinical and clinical results. The obtainable data suggest alterations of epigenetic regulation in schizophrenia. Moreover, they also showed the important role of epigenetic modifications in antipsychotic action. There is a need for more data to establish the role of epigenetic mechanisms in schizophrenia therapy. It would be of special interest to find and develop new targets for schizophrenia therapy because patients with schizophrenia could show little or no response to current pharmacotherapy and have treatment-resistant schizophrenia. Full article

Since their first application in psychiatry seventy years ago, antipsychotic drugs, besides schizophrenia, have been widely used in the treatment of mood disorders. Such an application of antipsychotics is the subject of this narrative review. Antipsychotic drugs can be arbitrarily classified into three generations. First-generation antipsychotics (FGAs), such as phenothiazines and haloperidol, were mainly applied for the treatment of acute mania, as well as psychotic depression when combined with antidepressants. The second-generation, so-called atypical antipsychotics (SGAs), such as clozapine, risperidone, olanzapine, and quetiapine, have antimanic activity and are also effective for the maintenance treatment of bipolar disorder. Additionally, quetiapine exerts therapeutic action in bipolar depression. Third-generation antipsychotics (TGAs) started with aripiprazole, a partial dopamine D2 receptor agonist, followed by brexpiprazole, lurasidone, cariprazine, and lumateperone. Out of these drugs, aripiprazole and cariprazine have antimanic activity, lurasidone, cariprazine, and lumateperone exert a significant antidepressant effect on bipolar depression, while there is evidence for the efficacy of aripiprazole and lurasidone in the prevention of recurrence in bipolar disorder. Therefore, successive generations of antipsychotic drugs present a diverse spectrum for application in mood disorders. Such a pharmacological overlap in the treatment of schizophrenia and bipolar illness stands in contrast to the dichotomous Kraepelinian division of schizophrenia and mood disorders. Full article

Intracranial hemorrhage (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper presents a multi-label ICH classification issue with six different types of hemorrhages, namely epidural (EPD), intraparenchymal (ITP), intraventricular (ITV), subarachnoid (SBC), subdural (SBD), and Some. A patient may experience numerous hemorrhages at the same time in some situations. A CT scan of a patient’s skull is used to detect and classify the type of ICH hemorrhage(s) present. First, our model determines whether there is a hemorrhage or not; if there is a hemorrhage, the model attempts to identify the type of hemorrhage(s). In this paper, we present a hybrid deep learning approach that combines convolutional neural network (CNN) and Long-Short Term Memory (LSTM) approaches (Conv-LSTM). In addition, to propose viable solutions for the problem, we used a Systematic Windowing technique with a Conv-LSTM. To ensure the efficacy of the proposed model, experiments are conducted on the RSNA dataset. The suggested model provides higher sensitivity (93.87%), specificity (96.45%), precision (95.21%), and accuracy (95.14%). In addition, the obtained F1 score results outperform existing deep neural network-based algorithms. Full article

Both Subjective Cognitive Decline (SCD) and mental fatigue are becoming increasingly prevalent as global demographics shifts indicate our aging populations. SCD is a reversible precursor for Alzheimer’s disease, and early identification is important for effective intervention strategies. We aim to investigate the association between mental fatigue—as well as other factors—and SCD. A total of 707 old adults (aged from 60 to 99) from Shanghai, China, participated in this study and completed self-reported instruments covering their cognitive and mental status as well as demographic information. Mental fatigue status was assessed by using four items derived from the functional impairment syndrome of the Old Adult Self Report (OASR). SCD was assessed by using the Memory/Cognition syndrome of OASR. A total of 681 old adults were included in the current study. The means of SCD significantly differed between each group of factors (age, gender, and mental fatigue). The general linear regression models showed that SCD increased with age, females scored higher than males, and SCD was positively associated with mental fatigue factors including difficulty getting things done, poor task performance, sleeping more, and a lack of energy among old adults. The study also found that SCD is negatively associated with the high-income group among young-old (aged from 60 to 75) males and associated with good marital/living status with the companion of spouses/partners among young-old females. These results suggest that gender, income level, marital/living status, and mental fatigue are crucial factors in preventing SCD among old adults and are pivotal in developing early intervention strategies to preserve the mental health of an increasingly aging population. Full article

Purpose: Brain tumors are diagnosed and classified manually and noninvasively by radiologists using Magnetic Resonance Imaging (MRI) data. The risk of misdiagnosis may exist due to human factors such as lack of time, fatigue, and relatively low experience. Deep learning methods have become increasingly important in MRI classification. To improve diagnostic accuracy, researchers emphasize the need to develop Computer-Aided Diagnosis (CAD) computational diagnostics based on artificial intelligence (AI) systems by using deep learning methods such as convolutional neural networks (CNN) and improving the performance of CNN by combining it with other data analysis tools such as wavelet transform. In this study, a novel diagnostic framework based on CNN and DWT data analysis is developed for the diagnosis of glioma tumors in the brain, among other tumors and other diseases, with T2-SWI MRI scans. It is a binary CNN classifier that treats the disease “glioma tumor” as positive and the other pathologies as negative, resulting in a very unbalanced binary problem. The study includes a comparative analysis of a CNN trained with wavelet transform data of MRIs instead of their pixel intensity values in order to demonstrate the increased performance of the CNN and DWT analysis in diagnosing brain gliomas. The results of the proposed CNN architecture are also compared with a deep CNN pre-trained on VGG16 transfer learning network and with the SVM machine learning method using DWT knowledge. Methods: To improve the accuracy of the CNN classifier, the proposed CNN model uses as knowledge the spatial and temporal features extracted by converting the original MRI images to the frequency domain by performing Discrete Wavelet Transformation (DWT), instead of the traditionally used original scans in the form of pixel intensities. Moreover, no pre-processing was applied to the original images. The images used are MRIs of type T2-SWI sequences parallel to the axial plane. Firstly, a compression step is applied for each MRI scan applying DWT up to three levels of decomposition. These data are used to train a 2D CNN in order to classify the scans as showing glioma or not. The proposed CNN model is trained on MRI slices originated from 382 various male and female adult patients, showing healthy and pathological images from a selection of diseases (showing glioma, meningioma, pituitary, necrosis, edema, non-enchasing tumor, hemorrhagic foci, edema, ischemic changes, cystic areas, etc.). The images are provided by the database of the Medical Image Computing and Computer-Assisted Intervention (MICCAI) and the Ischemic Stroke Lesion Segmentation (ISLES) challenges on Brain Tumor Segmentation (BraTS) challenges 2016 and 2017, as well as by the numerous records kept in the public general hospital of Chania, Crete, “Saint George”. Results: The proposed frameworks are experimentally evaluated by examining MRI slices originating from 190 different patients (not included in the training set), of which 56% are showing gliomas by the longest two axes less than 2 cm and 44% are showing other pathological effects or healthy cases. Results show convincing performance when using as information the spatial and temporal features extracted by the original scans. With the proposed CNN model and with data in DWT format, we achieved the following statistic percentages: accuracy 0.97, sensitivity (recall) 1, specificity 0.93, precision 0.95, FNR 0, and FPR 0.07. These numbers are higher for this data format (respectively: accuracy by 6% higher, recall by 11%, specificity by 7%, precision by 5%, FNR by 0.1%, and FPR is the same) than it would be, had we used as input data the intensity values of the MRIs (instead of the DWT analysis of the MRIs). Additionally, our study showed that when our CNN takes into account the TL of the existing network VGG, the performance values are lower, as follows: accuracy 0.87, sensitivity (recall) 0.91, specificity 0.84, precision 0.86, FNR of 0.08, and FPR 0.14. Conclusions: The experimental results show the outperformance of the CNN, which is not based on transfer learning, but is using as information the MRI brain scans decomposed into DWT information instead of the pixel intensity of the original scans. The results are promising for the proposed CNN based on DWT knowledge to serve for binary diagnosis of glioma tumors among other tumors and diseases. Moreover, the SVM learning model using DWT data analysis performs with higher accuracy and sensitivity than using pixel values. Full article

Pulmonary embolism (PE) is a significant consequence that is becoming more common in COVID-19 patients. The current study sought to determine the prevalence and risk factors for PE in a study population of COVID-19 patients, as well as the relationship between PE and neurological sequelae. The research also sought to analyze the consistency of neurological examination and imaging techniques in detecting neurological problems. The research comprised a total of 63 individuals with COVID-19. The incidence of PE in the study group was 9.5% for smokers, 23.8% for obese patients, 33.3% for hypertensive patients, and 19% for diabetic patients, according to the findings. After adjusting for possible confounders such as age, gender, BMI, smoking, hypertension, and diabetes, a logistic regression analysis indicated that the probabilities of having neurological complications were 3.5 times greater in individuals who had PE. In conclusion, the present study highlights the high incidence of PE among patients with COVID-19 and the association between PE and neurological complications. The study also emphasizes the importance of a thorough neurological examination and imaging studies in the detection of neurological complications in patients with PE. Full article

Physical exercise and cognitive remediation represent the psychosocial interventions with the largest basis of evidence attesting their effectiveness in improving cognitive performance in people living with schizophrenia according to recent international guidance. The aims of this review are to provide an overview of the literature on physical exercise as a treatment for cognitive impairment in schizophrenia and of the studies that have combined physical exercise and cognitive remediation as an integrated rehabilitation intervention. Nine meta-analyses and systematic reviews on physical exercise alone and seven studies on interventions combining physical exercise and cognitive remediation are discussed. The efficacy of physical exercise in improving cognitive performance in people living with schizophrenia is well documented, but more research focused on identifying moderators of participants response and optimal modalities of delivery is required. Studies investigating the effectiveness of integrated interventions report that combining physical exercise and cognitive remediation provides superior benefits and quicker improvements compared to cognitive remediation alone, but most studies included small samples and did not explore long-term effects. While physical exercise and its combination with cognitive remediation appear to represent effective treatments for cognitive impairment in people living with schizophrenia, more evidence is currently needed to better understand how to implement these treatments in psychiatric rehabilitation practice. Full article

Background: There is a growing liberalization of cannabis-based preparations for medical and recreational use. In multiple instances, anxiety and depression are cited as either a primary or a secondary reason for the use of cannabinoids. Aim: The purpose of this review is to explore the association between depression or anxiety and the dysregulation of the endogenous endocannabinoid system (ECS), as well as the use of phytocannabinoids and synthetic cannabinoids in the remediation of depression/anxiety symptoms. After a brief description of the constituents of cannabis, cannabinoid receptors and the endocannabinoid system, the most important evidence is presented for the involvement of cannabinoids in depression and anxiety both in human and from animal models of depression and anxiety. Finally, evidence is presented for the clinical use of cannabinoids to treat depression and anxiety. Conclusions: Although the common belief that cannabinoids, including cannabis, its main studied components—tetrahydrocannabinol (THC) and cannabidiol (CBD)—or other synthetic derivatives have been suggested to have a therapeutic role for certain mental health conditions, all recent systematic reviews that we report have concluded that the evidence that cannabinoids improve depressive and anxiety disorders is weak, of very-low-quality, and offers no guidance on the use of cannabinoids for mental health conditions within a regulatory framework. There is an urgent need for high-quality studies examining the effects of cannabinoids on mental disorders in general and depression/anxiety in particular, as well as the consequences of long-term use of these preparations due to possible risks such as addiction and even reversal of improvement. Full article

Depression and anxiety are highly prevalent in most neurological disorders and can have a major impact on the patient’s disability and quality of life. However, mostly due to the heterogeneity of symptoms and the complexity of the underlying comorbidities, depression can be difficult to diagnose, resulting in limited recognition and in undertreatment. The early detection and treatment of depression simultaneously with the neurological disorder is key to avoiding deterioration and further disability. Although the neurologist should be able to identify and treat depression initially, a neuropsychiatry team should be available for severe cases and those who are unresponsive to treatment. Neurologists should be also aware that in neurodegenerative diseases, such as Alzheimer’s or Parkinson’s, different depression symptoms could develop at different stages of the disease. The treatment options for depression in neurological diseases include drugs, cognitive-behavioral therapy, and somatic interventions, among others, but often, the evidence-based efficacy is limited and the results are highly variable. Here, we review recent research on the diagnosis and treatment of depression in the context of Alzheimer’s disease, Parkinson’s disease, and strokes, with the aim of identifying common approaches and solutions for its initial management by the neurologist. Full article

Deep learning has shown impressive diagnostic abilities in Alzheimer’s disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI. Full article

The need to identify new potentially druggable biochemical mechanisms for Alzheimer’s disease (AD) is an ongoing priority. The therapeutic limitations of amyloid-based approaches are further motivating this search. Amino acid metabolism, particularly tryptophan metabolism, has the potential to emerge as a leading candidate and an alternative exploitable biomolecular target. Multiple avenues support this contention. Tryptophan (trp) and its associated metabolites are able to inhibit various enzymes participating in the biosynthesis of β-amyloid, and one metabolite, 3-hydroxyanthranilate, is able to directly inhibit neurotoxic β-amyloid oligomerization; however, whilst certain trp metabolites are neuroprotectant, other metabolites, such as quinolinic acid, are directly toxic to neurons and may themselves contribute to AD progression. Trp metabolites also have the ability to influence microglia and associated cytokines in order to modulate the neuroinflammatory and neuroimmune factors which trigger pro-inflammatory cytotoxicity in AD. Finally, trp and various metabolites, including melatonin, are regulators of sleep, with disorders of sleep being an important risk factor for the development of AD. Thus, the involvement of trp biochemistry in AD is multifactorial and offers a plethora of druggable targets in the continuing quest for AD therapeutics. Full article

Advances in applied machine learning techniques for neuroimaging have encouraged scientists to implement models to diagnose brain disorders such as Alzheimer’s disease at early stages. Predicting the exact stage of Alzheimer’s disease is challenging; however, complex deep learning techniques can precisely manage this. While successful, these complex architectures are difficult to interrogate and computationally expensive. Therefore, using novel, simpler architectures with more efficient pattern extraction capabilities, such as transformers, is of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the group membership by separating healthy adults, mild cognitive impairment, and Alzheimer’s brains within the same age group (>75 years) using resting-state functional (rs-fMRI) and structural magnetic resonance imaging (sMRI) data aggressively preprocessed by our pipeline. Our optimized architecture, known as OViTAD is currently the sole vision transformer-based end-to-end pipeline and outperformed the existing transformer models and most state-of-the-art solutions. Our model achieved F1-scores of $97\%\pm 0.0$ and $99.55\%\pm 0.39$ from the testing sets for the rs-fMRI and sMRI modalities in the triple-class prediction experiments. Furthermore, our model reached these performances using 30% fewer parameters than a vanilla transformer. Furthermore, the model was robust and repeatable, producing similar estimates across three runs with random data splits (we reported the averaged evaluation metrics). Finally, to challenge the model, we observed how it handled increasing noise levels by inserting varying numbers of healthy brains into the two dementia groups. Our findings suggest that optimized vision transformers are a promising and exciting new approach for neuroimaging applications, especially for Alzheimer’s disease prediction. Full article

An inflammatory response after an aneurysmal subarachnoid hemorrhage (aSAH) has always been in the spotlight. However, few studies have compared the prognostic impact of inflammatory biomarkers. Moreover, why these inflammatory biomarkers contribute to a poor prognosis is also unclear. We retrospectively reviewed aSAH patients admitted to our institution between January 2015 and December 2020. The 90-day unfavorable functional outcome was defined as a modified Rankin scale (mRS) of ≥ 3. Independent inflammatory biomarker-related risk factors associated with 90-day unfavorable outcomes were derived from a forward stepwise multivariate analysis. Receiver operating characteristic curve analysis was conducted to identify the best cut-off value of inflammatory biomarkers. Then, patients were divided into two groups according to each biomarker’s cut-off value. To eliminate the imbalances in baseline characteristics, propensity score matching (PSM) was carried out to assess the impact of each biomarker on in-hospital complications. A total of 543 patients were enrolled in this study and 96 (17.7%) patients had unfavorable 90-day outcomes. A multivariate analysis showed that the white blood cell (WBC) count, the systemic inflammation response index, the neutrophil count, the neutrophil-to-albumin ratio, the monocyte count, and the monocyte-to-lymphocyte ratio were independently associated with 90-day unfavorable outcomes. The WBC count showed the best predictive ability (area under the curve (AUC) = 0.710, 95% CI = 0.652–0.769, p < 0.001). After PSM, almost all abnormal levels of inflammatory biomarkers were associated with a higher incidence of pneumonia during hospitalization. The WBC count had the strongest association with poor outcomes. Similar to nearly all other inflammatory biomarkers, the cause of poor prognosis may be the higher incidence of in-hospital pneumonia. Full article

The extent of resection beyond the enhancing core (EC) in glioblastoma IDH-wild type (GBM, IDHwt) is one of the most debated topics in neuro-oncology. Indeed, it has been demonstrated that local disease recurrence often arises in peritumoral areas and that radiologically-defined FLAIR hyperintensity areas of GBM IDHwt are often visible beyond the conventional EC. Therefore, the need to extend the surgical resection also to the FLAIR hyperintensity areas is a matter of debate. Since little is known about the histological composition of FLAIR hyperintensity regions, in this study we aimed to provide a comprehensive description of the histological features of EC and FLAIR hyperintensity regions sampled intraoperatively using neuronavigation and 5-aminolevulinic acid (5-ALA) fluorescence, in 33 patients with GBM, IDHwt. Assessing a total 109 histological samples, we found that FLAIR areas consisted in: (i) fragments of white matter focally to diffusely infiltrated by tumor cells in 76% of cases; (ii) a mixture of white matter with reactive astrogliosis and grey matter with perineuronal satellitosis in 15% and (iii) tumor tissue in 9%. A deeper knowledge of the histology of FLAIR hyperintensity areas in GBM, IDH-wt may serve to better guide neurosurgeons on the choice of the most appropriate surgical approach in patients with this neoplasm. Full article

It is a very alarming situation for the globe because 55 million humans are estimated to be affected by Alzheimer’s disease (AD) worldwide, and still it is increasing at the rapid speed of 10 million cases per year worldwide. This is an urgent reminder for better research and treatment due to the unavailability of a permanent medication for neurodegenerative disorders like AD. The lack of drugs for neurodegenerative disorder treatment is due to the complexity of the structure of the brain, mainly due to blood–brain barrier, because blood–brain drug molecules must enter the brain compartment. There are several novel and conventional formulation approaches that can be employed for the transportation of drug molecules to the target site in the brain, such as oral, intravenous, gene delivery, surgically implanted intraventricular catheter, nasal and liposomal hydrogels, and repurposing old drugs. A drug’s lipophilicity influences metabolic activity in addition to membrane permeability because lipophilic substances have a higher affinity for metabolic enzymes. As a result, the higher a drug’s lipophilicity is, the higher its permeability and metabolic clearance. AD is currently incurable, and the medicines available merely cure the symptoms or slow the illness’s progression. In the next 20 years, the World Health Organization (WHO) predicts that neurodegenerative illnesses affecting motor function will become the second-leading cause of mortality. The current article provides a brief overview of recent advances in brain drug delivery for AD therapy. Full article

Short sleep is a common issue nowadays. The purpose of this study was to investigate prefrontal cortical hemodynamics by evaluating changes in concentrations of oxygenated hemoglobin (HbO) in cognitive tests among short-sleep young adults and to explore the relationship between sleep duration, physical activity level, and cognitive function in this specific population. A total of 46 participants (25 males and 21 females) were included in our study, and among them, the average sleep duration was 358 min/day. Stroop performance in the short sleep population was linked to higher levels cortical activation in distinct parts of the left middle frontal gyrus. This study found that moderate-to-vigorous physical activity (MVPA) was significantly associated with lower accuracy of incongruent Stroop test. The dose-response relationship between sleep duration and Stroop performance under different levels of light-intensity physical activity (LPA) and MVPA was further explored, and increasing sleep time for different PA level was associated with better Stroop performance. In summary, this present study provided neurobehavioral evidence between cortical hemodynamics and cognitive function in the short sleep population. Furthermore, our findings indicated that, in younger adults with short sleep, more MVPA was associated with worse cognitive performance. Short sleep young adults should increase sleep time, rather than more MVPA, to achieve better cognitive function. Full article

Brain-derived neurotrophic factor (BDNF) belongs to the family of neurotrophins, which are growth factors with trophic effects on neurons. BDNF is the most widely distributed neurotrophin in the central nervous system (CNS) and is highly expressed in the prefrontal cortex (PFC) and hippocampus. Its distribution outside the CNS has also been demonstrated, but most studies have focused on its effects in neuropsychiatric disorders. Despite the advances in medicine in recent decades, neurological and psychiatric diseases are still characterized by high drug resistance. This review focuses on the use of BDNF in the developmental assessment, treatment monitoring, and pharmacotherapy of selected diseases, with a particular emphasis on epilepsy, depression, anorexia, obesity, schizophrenia, and Alzheimer’s disease. The limitations of using a molecule with such a wide distribution range and inconsistent method of determination are also highlighted. Full article

Synucleinopathies are a group of neurodegenerative diseases with common pathological lesions associated with the excessive accumulation and abnormal intracellular deposition of toxic species of α-synuclein. The shared clinical features are chronic progressive decline of motor, cognitive, and behavioral functions. These disorders include Parkinson’s disease, dementia with Lewy body, and multiple system atrophy. Vigorous research in the mechanisms of pathology of these illnesses is currently under way to find disease-modifying treatment and molecular markers for early diagnosis. α-Synuclein is a prone-to-aggregate, small amyloidogenic protein with multiple roles in synaptic vesicle trafficking, neurotransmitter release, and intracellular signaling events. Its expression is controlled by several mechanisms, one of which is epigenetic regulation. When transmitted to the nucleus, α-synuclein binds to DNA and histones and participates in epigenetic regulatory functions controlling specific gene transcription. Here, we discuss the various aspects of α-synuclein involvement in epigenetic regulation in health and diseases. Full article

Neurodegenerative diseases (ND) include a wide range of conditions that result from progressive damage to the neurons. Alzheimer’s disease (AD) is one of the most common NDs, and neuroinflammation and oxidative stress (OS) are the major factors in the development and progression of the disease. Many naturally occurring phytochemical compounds exhibit antioxidant and anti-inflammatory activities with potential neuroprotective effects. Several plant species, including Alpinia katsumadai and Alpinia conchigera, contain cardamonin (CD). CD (2′,4′-dihydroxy-6′methoxychalcone) has many therapeutic properties, including anticancer, anti-inflammatory, antioxidant, antiviral, and antibiotic activities. CD is a potent compound that can reduce OS and modulate the inflammatory processes that play a significant part in developing neurodegenerative diseases. CD has been shown to modulate a variety of signaling molecules involved in the development and progression of ND, including transcription factors (NF-kB and STAT3), cytokines (TNF-α, IL-1, and IL-6), enzymes (COX-2, MMP-9, and ALDH1), and other proteins and genes (Bcl-2, XIAP, and cyclin D1). Additionally, CD effectively modulates miRNA levels and autophagy-related CD-protective mechanisms against neurodegeneration. In summary, this review provides mechanistic insights into CD’s ability to modify multiple oxidative stress–antioxidant system pathways, Nrf2, and neuroinflammation. Additionally, it points to the possible therapeutic potential and preventive utilization of CD in neurodegenerative diseases, most specifically AD. Full article

Psychotic treatment-resistant depression is a complex and challenging manifestation of mood disorders in the clinical setting. Psychotic depression is a subtype of major depressive disorder characterized by mood-consistent hallucinations and/or delusions. Psychotic depression is often underdiagnosed and undertreated. Ketamine appears to have rapid and potent antidepressant effects in clinical studies, and the Federal Drug Agency approved the use of ketamine enantiomer esketamine-nasal spray for treatment-resistant depression pharmacotherapy in 2019. This study aimed to assess the usage of ketamine for major depressive disorder with psychotic features as an add-on treatment to the standard of care. Here we present four inpatients suffering from treatment-resistant depression with psychotic features, including one with severe suicidal crisis, all treated with 0.5 mg/kg intravenous infusion of ketamine. Subsequent monitoring revealed no exacerbation of psychotic symptoms in short and long-term observation, while stable remission was observed in all cases with imminent antisuicidal effect. Results suggest ketamine may benefit individuals with treatment-resistant depression with psychotic features. Full article

Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology. Full article

One of the most visible effects of aging, even in healthy, normal aging, is a decline in motor performance. The range of strategies applicable to counteract this deterioration has increased. Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique that can promote neuroplasticity, has recently gained attention. However, knowledge about optimized tDCS parameters in the elderly is limited. Therefore, in this study, we investigated the effect of different anodal tDCS intensities on motor sequence learning in the elderly. Over the course of four sessions, 25 healthy older adults (over 65 years old) completed the Serial Reaction Time Task (SRTT) while receiving 1, 2, or 3 mA of anodal or sham stimulation over the primary motor cortex (M1). Additionally, 24 h after stimulation, motor memory consolidation was assessed. The results confirmed that motor sequence learning in all tDCS conditions was maintained the following day. While increased anodal stimulation intensity over M1 showed longer lasting excitability enhancement in the elderly in a prior study, the combination of higher intensity stimulation with an implicit motor learning task showed no significant effect. Future research should focus on the reason behind this lack of effect and probe alternative stimulation protocols. Full article

Lithium has been a cornerstone treatment for bipolar disorder (BD). Despite descriptions in the literature regarding associations between long-term lithium therapy (LTLT) and development of a thyroid disorder (overt/subclinical hypo/hyperthyroidism, thyroid nodule, and goiter) in BD, factors such as time to onset of thyroid abnormalities and impact on clinical outcomes in the course of illness have not been fully characterized. In this study we aimed to compare clinical characteristics of adult BD patients with and without thyroid disorders who were on LTLT. We aimed to identify the incidence of thyroid disorders in patients with BD on LTLT and response to lithium between patients with and without thyroid disorders in BD. The Cox proportional model was used to find the median time to the development of a thyroid disorder. Our results showed that up to 32% of patients with BD on LTLT developed a thyroid disorder, of which 79% developed hypothyroidism, which was corrected with thyroid hormone replacement. We did not find significant differences in lithium response between patients with or without thyroid disorders in BD. Findings from this study suggest that patients with BD and comorbid thyroid disorders when adequately treated have a response to lithium similar to patients with BD and no thyroid disorders. Full article

Background: The neuroinflammation of the central nervous system (CNS) is a prevalent syndrome of brain dysfunction secondary to severe sepsis and is regulated by microglia. Triggering the receptor expressed on myeloid cells 2 (TREM2) is known to have protective functions that modulate the microglial polarization of M2 type to reduce inflammatory responses, thereby improving cognition. Methods: We examined the effect of TREM2 on the polarization state of microglia during the progression of neuroinflammation. After consecutive intraperitoneal injections of lipopolysaccharide for 7 days, we evaluated the inflammation of a septic mice model by hematoxylin–eosin (H&E) and electron microscopy, and we used immunofluorescence (IF) assays and Western blotting to visualize hippocampal sections in C57BL/6 mice to assess TREM2 expression. In addition, we analyzed the state of microglia polarization with quantitative RT-PCR. Result: The consecutive injection of LPS for 4 days elevated systemic inflammation and caused behavioral cognitive dysfunction in the septic model. However, on Day 7, the neuroinflammation was considerably attenuated. Meanwhile, TREM2 decreased on Day 4 and increased on Day 7 in vivo. Consistently, LPS could reduce the expression of TREM2 while IFN-β enhanced TREM2 expression in vitro. TREM2 regulated the microglial M1 phenotype’s conversion to the M2 phenotype. Conclusion: Our aim in this study was to investigate the interconnection between microglia polarization and TREM2 in neuroinflammation. Our results suggested that IFN-β could modulate TREM2 expression to alter the polarization state of microglia, thereby reducing LPS-induced neuroinflammation. Therefore, TREM2 is a novel potential therapeutic target for neuroinflammation. Full article

Nonspeech (or paraspeech) parameters are widely used in clinical assessment of speech impairment in persons with dysarthria (PWD). Virtually every standard clinical instrument used in dysarthria diagnostics includes nonspeech parameters, often in considerable numbers. While theoretical considerations have challenged the validity of these measures as markers of speech impairment, only a few studies have directly examined their relationship to speech parameters on a broader scale. This study was designed to investigate how nonspeech parameters commonly used in clinical dysarthria assessment relate to speech characteristics of dysarthria in individuals with movement disorders. Maximum syllable repetition rates, accuracies, and rates of isolated and repetitive nonspeech oral–facial movements and maximum phonation times were compared with auditory–perceptual and acoustic speech parameters. Overall, 23 diagnostic parameters were assessed in a sample of 130 patients with movement disorders of six etiologies. Each variable was standardized for its distribution and for age and sex effects in 130 neurotypical speakers. Exploratory Graph Analysis (EGA) and Confirmatory Factor Analysis (CFA) were used to examine the factor structure underlying the diagnostic parameters. In the first analysis, we tested the hypothesis that nonspeech parameters combine with speech parameters within diagnostic dimensions representing domain–general motor control principles. In a second analysis, we tested the more specific hypotheses that diagnostic parameters split along effector (lip vs. tongue) or functional (speed vs. accuracy) rather than task boundaries. Our findings contradict the view that nonspeech parameters currently used in dysarthria diagnostics are congruent with diagnostic measures of speech characteristics in PWD. Full article

These are the preliminary results of a 12-week non-randomized, open-label, non-inferiority study comparing the effectiveness of trazodone in an extended-release formulation (XR) versus SSRIs in the treatment of major depressive disorder (MDD). Participants (n = 76) were recruited, and 42 were assigned to the trazodone XR group and 34 to the SSRIs group. The choice of drug was based on clinical presentation and relied upon the attending physician. Assessments were made at five observation time points, at the following weeks: 0, and after 2, 4, 8, and 12 weeks. The evaluations included: symptoms of depression (MADRS, QIDS-clinician, and self-rated versions-primary study endpoints), anhedonia (SHAPS), anxiety (HAM-A), insomnia (AIS), psychosocial functioning (SDS), and therapeutic efficacy (CGI). At baseline, the trazodone group had significantly more severe depressive, anxiety, and insomnia symptoms and worse psychosocial functioning compared to the SSRIs group. After 12 weeks, trazodone XR was more effective than SSRIs in reducing the severity of insomnia and depression. There were no differences between the groups in the frequencies of therapeutic response and remission, which indicated the non-inferiority of the trazodone XR treatment. In conclusion, our results showed that in a “real world” setting, trazodone XR is effective in the treatment of patients with MDD. Full article

There is currently no simple, widely available screening method for Alzheimer’s disease (AD), partly because the diagnosis of AD is complex and typically involves expensive and sometimes invasive tests not commonly available outside highly specialized clinical settings. Here, we developed an artificial intelligence (AI)-powered end-to-end system to detect AD and predict its severity directly from voice recordings. At the core of our system is the pre-trained data2vec model, the first high-performance self-supervised algorithm that works for speech, vision, and text. Our model was internally evaluated on the ADReSSo (Alzheimer’s Dementia Recognition through Spontaneous Speech only) dataset containing voice recordings of subjects describing the Cookie Theft picture, and externally validated on a test dataset from DementiaBank. The AI model can detect AD with average area under the curve (AUC) of 0.846 and 0.835 on held-out and external test set, respectively. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.9616). Moreover, the model can reliably predict the subject’s cognitive testing score solely based on raw voice recordings. Our study demonstrates the feasibility of using the AI-powered end-to-end model for early AD diagnosis and severity prediction directly based on voice, showing its potential for screening Alzheimer’s disease in a community setting. Full article

Working memory (WM) plays a crucial role in the development of arithmetic ability. However, research findings related to which factors influence the relationship between WM and arithmetic skills are inconsistent. The present meta-analysis aimed to examine the links between WM and arithmetic in primary school children and investigate whether this is dependent on WM domains (i.e., verbal, visual, spatial), child age, arithmetic operation type, and arithmetic task type. A total of 11,224 participants with an age range of 6- to 12 years, from 55 independent samples were included in the meta-analysis. Analysis of 46 studies with 187 effect sizes revealed an overall significant and medium correlation between WM and arithmetic. Heterogeneity analyses indicated that verbal WM showed a stronger correlation with arithmetic than visuospatial WM, and that correlations between verbal WM and arithmetic declined with age, whereas correlations between spatial-sequential, and spatial-simultaneous WM and arithmetic remained stable throughout development. Addition and subtraction were more involved in verbal WM than multiplication and division. Moreover, mental and written arithmetic showed comparable correlations with WM in all domains. These findings suggest moderation effects of WM domains, age, and operation types in the WM-arithmetic relationship and highlight the significant role of verbal WM in arithmetic ability in primary school children. Full article

It is hypothesized that factors associated with bipolar disorder could, uer defined conditions, produce adaptive behaviors. The aim is to verify whether a genetic feature associated with bipolar disorder can be found in people without bipolar disorder but with hyperactivity/exploration traits. Healthy old adults (N = 40) recruited for a previous study on exercise were subdivided using a previously validated tool into those with and without hyperactivity/exploration traits and compared with a group of old patients with bipolar disorder (N = 21). The genetic variant RS1006737 of CACNA1C was analyzed using blood samples, DNA extraction, real-time PCR, FRET probes, and SANGER method sequencing. People with hyperactivity/exploration traits and without bipolar disorder were like people with bipolar disorder regarding the frequency of the genetic variant (OR = 0.79, CI95%: 0.21–2.95), but were different from people without either hyperactivity/exploration traits and bipolar disorder (OR = 4.75, CI95%: 1.19–18.91). The combined group of people with hyperactivity/exploration traits without bipolar disorder plus people with bipolar disorder had a higher frequency of the variant than people without either hyperactivity/exploration traits or bipolar disorder (OR = 4.25, CI95%: 1.24–14.4). To consider the genetic profile of bipolar disorder not an aberrant condition opens the way to a new approach in which the adaptive potential would be a central point in psychosocial treatment in addition to drug therapy. Future research can confirm the results of our study. Full article

Motor and cognitive rehabilitation in individuals with traumatic brain injury (TBI) is a growing field of clinical and research interest. In fact, novel rehabilitative approaches allow a very early verticalization and gait training through robotic devices and other innovative tools boosting neuroplasticity, thanks to the high-intensity, repetitive and task-oriented training. In the same way, cognitive rehabilitation is also evolving towards advanced interventions using virtual reality (VR), computer-based approaches, telerehabilitation and neuromodulation devices. This review aimed to systematically investigate the existing evidence concerning the role of innovative technologies in the motor and cognitive neurorehabilitation of TBI patients. We searched and reviewed the studies published in the Cochrane Library, PEDro, PubMed and Scopus between January 2012 and September 2022. After an accurate screening, only 29 papers were included in this review. This systematic review has demonstrated the beneficial role of innovative technologies when applied to cognitive rehabilitation in patients with TBI, while evidence of their effect on motor rehabilitation in this patient population is poor and still controversial. Full article

This article provides an overview of verbal lie detection research. This type of research began in the 1970s with examining the relationship between deception and specific words. We briefly review this initial research. In the late 1980s, Criteria-Based Content Analysis (CBCA) emerged, a veracity assessment tool containing a list of verbal criteria. This was followed by Reality Monitoring (RM) and Scientific Content Analysis (SCAN), two other veracity assessment tools that contain lists of verbal criteria. We discuss their contents, theoretical rationales, and ability to identify truths and lies. We also discuss similarities and differences between CBCA, RM, and SCAN. In the mid 2000s, ‘Interviewing to deception’ emerged, with the goal of developing specific interview protocols aimed at enhancing or eliciting verbal veracity cues. We outline the four most widely researched interview protocols to date: the Strategic Use of Evidence (SUE), Verifiability Approach (VA), Cognitive Credibility Assessment (CCA), and Reality Interviewing (RI). We briefly discuss the working of these protocols, their theoretical rationales and empirical support, as well as the similarities and differences between them. We conclude this article with elaborating on how neuroscientists can inform and improve verbal lie detection. Full article

The COVID-19 virus frequently causes neurological complications. These have been described in various forms in adults and children. Headache, seizures, coma, and encephalitis are some of the manifestations of SARS-CoV-2-induced neurological impairment. Recent publications have revealed important aspects of viral pathophysiology and its involvement in nervous-system impairment in humans. We evaluated the latest literature describing the relationship between COVID-19 infection and the central nervous system. We searched three databases for observational and interventional studies in adults published between December 2019 and September 2022. We discussed in narrative form the neurological impairment associated with COVID-19, including clinical signs and symptoms, imaging abnormalities, and the pathophysiology of SARS-CoV2-induced neurological damage. Full article

(1) Background: Functional Hypothalamic Amenorrhea (FHA) can be caused by the hyper activation of neuro-endocrine responses to stress. Among other endocrine factors and hypothalamic dysfunctions, the psychophysiological stress response can very frequently lead to an inhibition of the gonadal–pituitary axis. The aim of this study was to investigate the level of neurovegetative activation in a group of young women affected by this condition. (2) Methods: Twenty-five women (mean age = 21.1 ± 4.34) with FHA were consecutively recruited. Information on psycho-physiological distress was collected through a Psychopathological assessment (with the administration of three psychometric tests) and the Psychophysiological Stress Profile (PSP). Their data were compared with a control group. (3) Results: In the PSP, the patients displayed significantly higher values compared to controls in terms of the parameters of muscle tension (sEMG), skin conductance (SCL/SCR), heart rate (HR), and peripheral temperature (PT). Furthermore, autonomic hyper-activation at rest, marked reactivity to stress, and reduced recovery were seen. Moreover, a condition characterized by psychological distress (anxiety and somatic complaints, depressed and irritable mood, obsessive-compulsive traits) emerged. (4) Conclusions: The results highlight autonomic hyper-activation in FHA, which is also associated with psychological distress. Considering that FHA is a condition that affects multiple systems between mind and body, a multimodal, multidimensional, and multidisciplinary assessment of stress is becoming an emerging need. Full article

Background: Huntington’s disease is an inherited autosomal dominant trait neuro-degenerative disorder caused by changes (mutations) of a gene called huntingtin (htt) that is located on the short arm (p) of chromosome 4, CAG expansion mutation. It is characterized by unusual movements, cognitive and psychiatric disorders. Objective: This review was undertaken to apprehend biological pathways of Huntington’s disease (HD) pathogenesis and its management by nature-derived products. Natural products can be lucrative for the management of HD as it shows protection against HD in pre-clinical trials. Advanced research is still required to assess the therapeutic effectiveness of the known organic products and their isolated compounds in HD experimental models. Summary: Degeneration of neurons in Huntington’s disease is distinguished by progressive loss of motor coordination and muscle function. This is due to the expansion of CAG trinucleotide in the first exon of the htt gene responsible for neuronal death and neuronal network degeneration in the brain. It is believed that the factors such as molecular genetics, oxidative stress, excitotoxicity, mitochondrial dysfunction, neuroglia dysfunction, protein aggregation, and altered UPS leads to HD. The defensive effect of the natural product provides therapeutic efficacy against HD. Recent reports on natural drugs have enlightened the protective role against HD via antioxidant, anti-inflammatory, antiapoptotic, and neurofunctional regulation. Full article

A better understanding of biological and emotional variables associated with health-related quality of life in people with long-COVID is needed. Our aim was to identify potential direct and indirect effects on the relationships between sensitization-associated symptoms, mood disorders such as anxiety/depressive levels, and sleep quality on health-related quality of life in people suffering from post-COVID-19 pain. One hundred and forty-six individuals who were hospitalized due to COVID-19 during the first wave of the pandemic and suffering from long-term post-COVID-19 pain completed different patient-reported outcome measures (PROMs), including clinical features, symptoms associated with sensitization of the central nervous system (Central Sensitization Inventory), mood disorders (Hospital Anxiety and Depressive Scale), sleep quality (Pittsburgh Sleep Quality Index), and health-related quality of life (paper-based five-level version of EuroQol-5D) in a face-to-face interview conducted at 18.8 (SD 1.8) months after hospitalization. Different mediation models were conducted to assess the direct and indirect effects of the associations among the different variables. The mediation models revealed that sensitization-associated symptoms and depressive levels directly affected health-related quality of life; however, these effects were not statistically significant when sleep quality was included. In fact, the effect of sensitization-associated symptomatology on quality of life (β = −0.10, 95% CI −0.1736, −0.0373), the effect of depressive levels on quality of life (β= −0.09, 95% CI −0.1789, −0.0314), and the effect of anxiety levels on quality of life (β = −0.09, 95% CI −0.1648, −0.0337) were all indirectly mediated by sleep quality. This study revealed that sleep quality mediates the relationship between sensitization-associated symptoms and mood disorders (depressive/anxiety levels) with health-related quality of life in individuals who were hospitalized with COVID-19 at the first wave of the pandemic and reporting post-COVID-19 pain. Longitudinal studies will help to determine the clinical implications of these findings. Full article