Showing papers in "Brain Sciences in 2019"
TL;DR: Current understanding of the role of disrupted emotion regulation in adolescent anxiety and depression is reviewed, describing findings from self-report, behavioral, peripheral psychophysiological, and neural measures and approaches to improve treatments based on empirical evidence are discussed.
Abstract: Emotion regulation skills develop substantially across adolescence, a period characterized by emotional challenges and developing regulatory neural circuitry. Adolescence is also a risk period for the new onset of anxiety and depressive disorders, psychopathologies which have long been associated with disruptions in regulation of positive and negative emotions. This paper reviews the current understanding of the role of disrupted emotion regulation in adolescent anxiety and depression, describing findings from self-report, behavioral, peripheral psychophysiological, and neural measures. Self-report studies robustly identified associations between emotion dysregulation and adolescent anxiety and depression. Findings from behavioral and psychophysiological studies are mixed, with some suggestion of specific impairments in reappraisal in anxiety. Results from neuroimaging studies broadly implicate altered functioning of amygdala-prefrontal cortical circuitries, although again, findings are mixed regarding specific patterns of altered neural functioning. Future work may benefit from focusing on designs that contrast effects of specific regulatory strategies, and isolate changes in emotional regulation from emotional reactivity. Approaches to improve treatments based on empirical evidence of disrupted emotion regulation in adolescents are also discussed. Future intervention studies might consider training and measurement of specific strategies in adolescents to better understand the role of emotion regulation as a treatment mechanism.
189 citations
TL;DR: Two-dimensional frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes and Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature.
Abstract: The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.
100 citations
TL;DR: The novel ‘big data’ approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction are summarised, and recommendations for future direction are concluded.
Abstract: Quantifying gait and postural control adds valuable information that aids in understanding neurological conditions where motor symptoms predominate and cause considerable functional impairment. Disease-specific clinical scales exist; however, they are often susceptible to subjectivity, and can lack sensitivity when identifying subtle gait and postural impairments in prodromal cohorts and longitudinally to document disease progression. Numerous devices are available to objectively quantify a range of measurement outcomes pertaining to gait and postural control; however, efforts are required to standardise and harmonise approaches that are specific to the neurological condition and clinical assessment. Tools are urgently needed that address a number of unmet needs in neurological practice. Namely, these include timely and accurate diagnosis; disease stratification; risk prediction; tracking disease progression; and decision making for intervention optimisation and maximising therapeutic response (such as medication selection, disease staging, and targeted support). Using some recent examples of research across a range of relevant neurological conditions-including Parkinson's disease, ataxia, and dementia-we will illustrate evidence that supports progress against these unmet clinical needs. We summarise the novel 'big data' approaches that utilise data mining and machine learning techniques to improve disease classification and risk prediction, and conclude with recommendations for future direction.
97 citations
TL;DR: Clinicians are provided with pragmatic suggestions and tools to identify, and manage this prevalent COMISA disorder in clinical settings, and discuss future avenues of research to progress the field.
Abstract: Co-morbid insomnia and sleep apnea (COMISA) is a highly prevalent and debilitating disorder, which results in additive impairments to patients’ sleep, daytime functioning, and quality of life, and complex diagnostic and treatment decisions for clinicians. Although the presence of COMISA was first recognized by Christian Guilleminault and colleagues in 1973, it received very little research attention for almost three decades, until the publication of two articles in 1999 and 2001 which collectively reported a 30%–50% co-morbid prevalence rate, and re-ignited research interest in the field. Since 1999, there has been an exponential increase in research documenting the high prevalence, common characteristics, treatment complexities, and bi-directional relationships of COMISA. Recent trials indicate that co-morbid insomnia symptoms may be treated with cognitive and behavioral therapy for insomnia, to increase acceptance and use of continuous positive airway pressure therapy. Hence, the treatment of COMISA appears to require nuanced diagnostic considerations, and multi-faceted treatment approaches provided by multi-disciplinary teams of psychologists and physicians. In this narrative review, we present a brief overview of the history of COMISA research, describe the importance of measuring and managing insomnia symptoms in the presence of sleep apnea, discuss important methodological and diagnostic considerations for COMISA, and review several recent randomized controlled trials investigating the combination of CBTi and CPAP therapy. We aim to provide clinicians with pragmatic suggestions and tools to identify, and manage this prevalent COMISA disorder in clinical settings, and discuss future avenues of research to progress the field.
86 citations
TL;DR: The timing of acute exercise plays an important role in the exercise-memory interaction and various exercise- and participant-related characteristics moderate this temporal relationship.
Abstract: Background: Accumulating research demonstrates that the timing of exercise plays an important role in influencing episodic memory. However, we have a limited understanding as to the factors that moderate this temporal effect. Thus, the purpose of this systematic review with meta-analysis was to evaluate the effects of study characteristics (e.g., exercise modality, intensity and duration of acute exercise) and participant attributes (e.g., age, sex) across each of the temporal periods of acute exercise on episodic memory (i.e., acute exercise occurring before memory encoding, and during memory encoding, early consolidation, and late consolidation). Methods: The following databases were used for our computerized searches: Embase/PubMed, Web of Science, Google Scholar, Sports Discus and PsychInfo. Studies were included if they: (1) Employed an experimental design with a comparison to a control group/visit, (2) included human participants, (3) evaluated exercise as the independent variable, (4) employed an acute bout of exercise (defined as a single bout of exercise), (5) evaluated episodic memory as the outcome variable (defined as the retrospective recall of information either in a spatial or temporal manner), and (6) provided sufficient data (e.g., mean, SD, and sample size) for a pooled effect size estimate. Results: In total, 25 articles met our inclusionary criteria and were meta-analyzed. Acute exercise occurring before memory encoding (d = 0.11, 95% CI: −0.01, 0.23, p = 0.08), during early memory consolidation (d = 0.47, 95% CI: 0.28, 0.67; p < 0.001) and during late memory consolidation (d = 1.05, 95% CI: 0.32, 1.78; p = 0.005) enhanced episodic memory function. Conversely, acute exercise occurring during memory encoding had a negative effect on episodic memory (d = −0.12, 95% CI: −0.22, −0.02; p = 0.02). Various study designs and participant characteristics moderated the temporal effects of acute exercise on episodic memory function. For example, vigorous-intensity acute exercise, and acute exercise among young adults, had greater effects when the acute bout of exercise occurred before memory encoding or during the early memory consolidation period. Conclusions: The timing of acute exercise plays an important role in the exercise-memory interaction. Various exercise- and participant-related characteristics moderate this temporal relationship.
77 citations
TL;DR: The main findings suggest that moderately depressed samples exhibit the largest interoceptive deficits as compared with healthy adults, and difficulties in decision making and low affect intensity are correlated with low IAc, and IAc seems to normalize in severely depressed subjects.
Abstract: Interoception is the sense of the physiological condition of the entire body. Impaired interoception has been associated with aberrant activity of the insula in major depressive disorder (MDD) during heartbeat perception tasks. Despite clinical relevance, studies investigating interoceptive impairments in MDD have never been reviewed systematically according to the guidelines of the PRISMA protocol, and therefore we collated studies that assessed accuracy in detecting heartbeat sensations (interoceptive accuracy, IAc) in MDD (databases: PubMed/Medline, PsycINFO, and PsycARTICLES). Out of 389 records, six studies met the inclusion criteria. The main findings suggest that (i) moderately depressed samples exhibit the largest interoceptive deficits as compared with healthy adults. (ii) difficulties in decision making and low affect intensity are correlated with low IAc, and (iii) IAc seems to normalize in severely depressed subjects. These associations may be confounded by sex, anxiety or panic disorder, and intake of selective serotonin reuptake inhibitors. Our findings have implications for the development of interoceptive treatments that might relieve MDD-related symptoms or prevent relapse in recurrent depression by targeting the interoceptive nervous system.
76 citations
TL;DR: A gradient boosted machine (GBM) model may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
Abstract: Background. Alzheimer’s is a disease for which there is no cure. Diagnosing Alzheimer’s disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI’s (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.
69 citations
TL;DR: This review focuses primarily on two regions—the PFC and the amygdala—and discusses how changes in plasticity, synaptic transmission, inhibition/excitation, and connectivity (including modulation by hippocampal afferents to the PFC) may contribute to transient deficits in extinction retention.
Abstract: Anxiety disorders that develop in adolescence represent a significant burden and are particularly challenging to treat, due in no small part to the high occurrence of relapse in this age group following exposure therapy. This pattern of persistent fear is preserved across species; relative to those younger and older, adolescents consistently show poorer extinction, a key process underpinning exposure therapy. This suggests that the neural processes underlying fear extinction are temporarily but profoundly compromised during adolescence. The formation, retrieval, and modification of fear- and extinction-associated memories are regulated by a forebrain network consisting of the prefrontal cortex (PFC), the amygdala, and the hippocampus. These regions undergo robust maturational changes in early life, with unique alterations in structure and function occurring throughout adolescence. In this review, we focus primarily on two of these regions—the PFC and the amygdala—and discuss how changes in plasticity, synaptic transmission, inhibition/excitation, and connectivity (including modulation by hippocampal afferents to the PFC) may contribute to transient deficits in extinction retention. We end with a brief consideration of how exposure to stress during this adolescent window of vulnerability can permanently disrupt neurodevelopment, leading to lasting impairments in pathways of emotional regulation.
68 citations
TL;DR: In general, it is found neuro Stimulation is safe and effective, although any high quality evidence applying neurostimulation to pediatrics is lacking.
Abstract: Neurostimulation for epilepsy refers to the application of electricity to affect the central nervous system, with the goal of reducing seizure frequency and severity. We review the available evidence for the use of neurostimulation to treat pediatric epilepsy, including vagus nerve stimulation (VNS), responsive neurostimulation (RNS), deep brain stimulation (DBS), chronic subthreshold cortical stimulation (CSCS), transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS). We consider possible mechanisms of action and safety concerns, and we propose a methodology for selecting between available options. In general, we find neurostimulation is safe and effective, although any high quality evidence applying neurostimulation to pediatrics is lacking. Further research is needed to understand neuromodulatory systems, and to identify biomarkers of response in order to establish optimal stimulation paradigms.
66 citations
TL;DR: The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.
Abstract: The classification recognition rate of motor imagery is a key factor to improve the performance of brain–computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the sub-band signal is decomposed with EMD to get a set of stationary time series, which are called intrinsic mode functions (IMFs). Secondly, the appropriate IMFs for signal reconstruction are selected. Thus, the approximate entropy of the reconstructed signal can be obtained as the corresponding feature vector. Finally, support vector machine (SVM) is used to perform the classification. The proposed method solves the problem of wide frequency band coverage during EMD and further improves the classification accuracy of EEG signal motion imaging.
64 citations
TL;DR: A deep learning approach is used to discriminate between healthy people and the two types of MCI groups based on MRI results, using the convolutional neural network with an efficient architecture to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups.
Abstract: Mild cognitive impairment (MCI) is an intermediary stage condition between healthy people and Alzheimer’s disease (AD) patients and other dementias. AD is a progressive and irreversible neurodegenerative disorder, which is a significant threat to people, age 65 and older. Although MCI does not always lead to AD, an early diagnosis at the stage of MCI can be very helpful in identifying people who are at risk of AD. Moreover, the early diagnosis of MCI can lead to more effective treatment, or at least, significantly delay the disease’s progress, and can lead to social and financial benefits. Magnetic resonance imaging (MRI), which has become a significant tool for the diagnosis of MCI and AD, can provide neuropsychological data for analyzing the variance in brain structure and function. MCI is divided into early and late MCI (EMCI and LMCI) and sadly, there is no clear differentiation between the brain structure of healthy people and MCI patients, especially in the EMCI stage. This paper aims to use a deep learning approach, which is one of the most powerful branches of machine learning, to discriminate between healthy people and the two types of MCI groups based on MRI results. The convolutional neural network (CNN) with an efficient architecture was used to extract high-quality features from MRIs to classify people into healthy, EMCI, or LMCI groups. The MRIs of 600 individuals used in this study included 200 control normal (CN) people, 200 EMCI patients, and 200 LMCI patients. This study randomly selected 70 percent of the data to train our model and 30 percent for the test set. The results showed the best overall classification between CN and LMCI groups in the sagittal view with an accuracy of 94.54 percent. In addition, 93.96 percent and 93.00 percent accuracy were reached for the pairs of EMCI/LMCI and CN/EMCI, respectively.
TL;DR: There is meta-analytic evidence supporting a significant association between attention-deficit/hyperactivity disorder and obesity, at least in adults, and empirical evidence guiding the management/treatment of patients with the two conditions is still limited.
Abstract: This narrative review is aimed at presenting the most recent evidence on the association between attention-deficit/hyperactivity disorder (ADHD) and obesity. The review is informed by previous relevant systematic reviews and a search in Pubmed and PsycINFO up to 1 August 2019. Although the association between ADHD and obesity would seem, at first, paradoxical, in the past two decades there has been an increasing number of studies on this topic. The present review shows that there is meta-analytic evidence supporting a significant association between these two conditions, at least in adults. Growing evidence is also being published on the genetic and environmental factors underlying the association. However, the cause–effects paths, as well as the exact mechanisms explaining the association, remain unclear. Additionally, empirical evidence guiding the management/treatment of patients with the two conditions is still limited. Therefore, after almost 20 years from the first report of a link between ADHD and obesity, this association continues to be puzzling.
TL;DR: An updated review on spinal cord stimulation, dorsal root ganglion stimulation, and peripheral nerve stimulation focuses on mechanisms of action, clinical indications, and future areas of research.
Abstract: The field of neuromodulation has seen unprecedented growth over the course of the last decade with novel waveforms, hardware advancements, and novel chronic pain indications. We present here an updated review on spinal cord stimulation, dorsal root ganglion stimulation, and peripheral nerve stimulation. We focus on mechanisms of action, clinical indications, and future areas of research. We also present current drawbacks with current stimulation technology and suggest areas of future advancements. Given the current shortage of viable treatment options using a pharmacological based approach and conservative interventional therapies, neuromodulation presents an interesting area of growth and development for the interventional pain field and provides current and future practitioners a fresh outlook with regards to its place in the chronic pain treatment paradigm.
TL;DR: This paper examines core constructs of ASI identified in the seminal work of Jean Ayres, and presents current neuroscience research that underlies the main patterns of sensory integration function and dysfunction, proposing neuroplasticity as the mechanisms underlying change as a result of ASi intervention.
Abstract: Sensory integration, now trademarked as Ayres Sensory Integration® or ASI, is based on principles of neuroscience and provides a framework for understanding the contributions of the sensory and motor foundations of human behavior. The theory and practice of ASI continues to evolve as greater understanding of the neurobiology of human behavior emerges. In this paper we examine core constructs of ASI identified in the seminal work of Dr. Jean Ayres, and present current neuroscience research that underlies the main patterns of sensory integration function and dysfunction. We consider how current research verifies and clarifies Ayres’ propositions by describing functions of the vestibular, proprioceptive, and tactile sensory systems, and exploring their relationships to ocular, postural, bilateral integration, praxis, and sensory modulation. We close by proposing neuroplasticity as the mechanisms underlying change as a result of ASI intervention.
TL;DR: Throughout this review multiple endeavors to demonstrate how T2DM increases the vulnerability of the brain’s neurovascular unit, neuroglia and neurons are presented.
Abstract: Type 2 diabetes mellitus (T2DM) and late-onset Alzheimer’s disease–dementia (LOAD) are increasing in global prevalence and current predictions indicate they will only increase over the coming decades. These increases may be a result of the concurrent increases of obesity and aging. T2DM is associated with cognitive impairments and metabolic factors, which increase the cellular vulnerability to develop an increased risk of age-related LOAD. This review addresses possible mechanisms due to obesity, aging, multiple intersections between T2DM and LOAD and mechanisms for the continuum of progression. Multiple ultrastructural images in female diabetic db/db models are utilized to demonstrate marked cellular remodeling changes of mural and glia cells and provide for the discussion of functional changes in T2DM. Throughout this review multiple endeavors to demonstrate how T2DM increases the vulnerability of the brain’s neurovascular unit (NVU), neuroglia and neurons are presented. Five major intersecting links are considered: i. Aging (chronic age-related diseases); ii. metabolic (hyperglycemia advanced glycation end products and its receptor (AGE/RAGE) interactions and hyperinsulinemia-insulin resistance (a linking linchpin); iii. oxidative stress (reactive oxygen–nitrogen species); iv. inflammation (peripheral macrophage and central brain microglia); v. vascular (macrovascular accelerated atherosclerosis—vascular stiffening and microvascular NVU/neuroglial remodeling) with resulting impaired cerebral blood flow.
TL;DR: It is critical that more research specifically focuses on females with FXS to elucidate the role of genetic, environmental, and socio-emotional factors on outcome in this often-overlooked population.
Abstract: Fragile X syndrome (FXS) is a genetic condition known to increase the risk of cognitive impairment and socio-emotional challenges in affected males and females. To date, the vast majority of research on FXS has predominantly targeted males, who usually exhibit greater cognitive impairment compared to females. Due to their typically milder phenotype, females may have more potential to attain a higher level of independence and quality of life than their male counterparts. However, the constellation of cognitive, behavioral, and, particularly, socio-emotional challenges present in many females with FXS often preclude them from achieving their full potential. It is, therefore, critical that more research specifically focuses on females with FXS to elucidate the role of genetic, environmental, and socio-emotional factors on outcome in this often-overlooked population.
TL;DR: Key aspects of the neuroinflammatory response that have been implicated in epilepsy are focused on, with a particular focus on post-traumatic epilepsy (PTE), which has the potential to unveil new drug targets for PTE, and identify immune-based biomarkers for improved epilepsy prediction.
Abstract: Epilepsy is a common chronic consequence of traumatic brain injury (TBI), contributing to increased morbidity and mortality for survivors. As post-traumatic epilepsy (PTE) is drug-resistant in at least one-third of patients, there is a clear need for novel therapeutic strategies to prevent epilepsy from developing after TBI, or to mitigate its severity. It has long been recognized that seizure activity is associated with a local immune response, characterized by the activation of microglia and astrocytes and the release of a plethora of pro-inflammatory cytokines and chemokines. More recently, increasing evidence also supports a causal role for neuroinflammation in seizure induction and propagation, acting both directly and indirectly on neurons to promote regional hyperexcitability. In this narrative review, we focus on key aspects of the neuroinflammatory response that have been implicated in epilepsy, with a particular focus on PTE. The contributions of glial cells, blood-derived leukocytes, and the blood–brain barrier will be explored, as well as pro- and anti-inflammatory mediators. While the neuroinflammatory response to TBI appears to be largely pro-epileptogenic, further research is needed to clearly demonstrate causal relationships. This research has the potential to unveil new drug targets for PTE, and identify immune-based biomarkers for improved epilepsy prediction.
TL;DR: This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born) and shows that the novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs.
Abstract: Magnetic resonance imaging (MRI) is a common imaging technique used extensively to study human brain activities. Recently, it has been used for scanning the fetal brain. Amongst 1000 pregnant women, 3 of them have fetuses with brain abnormality. Hence, the primary detection and classification are important. Machine learning techniques have a large potential in aiding the early detection of these abnormalities, which correspondingly could enhance the diagnosis process and follow up plans. Most research focused on the classification of abnormal brains in a primary age has been for newborns and premature infants, with fewer studies focusing on images for fetuses. These studies associated fetal scans to scans after birth for the detection and classification of brain defects early in the neonatal age. This type of brain abnormality is named small for gestational age (SGA). This article proposes a novel framework for the classification of fetal brains at an early age (before the fetus is born). As far as we could know, this is the first study to classify brain abnormalities of fetuses of widespread gestational ages (GAs). The study incorporates several machine learning classifiers, such as diagonal quadratic discriminates analysis (DQDA), K-nearest neighbour (K-NN), random forest, naive Bayes, and radial basis function (RBF) neural network classifiers. Moreover, several bagging and Adaboosting ensembles models have been constructed using random forest, naive Bayes, and RBF network classifiers. The performances of these ensembles have been compared with their individual models. Our results show that our novel approach can successfully identify and classify numerous types of defects within MRI images of the fetal brain of various GAs. Using the KNN classifier, we were able to achieve the highest classification accuracy and area under receiving operating characteristics of 95.6% and 99% respectively. In addition, ensemble classifiers improved the results of their respective individual models.
TL;DR: The neural underpinnings of sensory processing and integration in ASD are reviewed by examining the literature on neurophysiological responses to sensory stimuli in individuals with ASD as well as structural and network organization using a variety of neuroimaging techniques.
Abstract: Abnormal sensory-based behaviors are a defining feature of autism spectrum disorders (ASD). Dr. A. Jean Ayres was the first occupational therapist to conceptualize Sensory Integration (SI) theories and therapies to address these deficits. Her work was based on neurological knowledge of the 1970’s. Since then, advancements in neuroimaging techniques make it possible to better understand the brain areas that may underlie sensory processing deficits in ASD. In this article, we explore the postulates proposed by Ayres (i.e., registration, modulation, motivation) through current neuroimaging literature. To this end, we review the neural underpinnings of sensory processing and integration in ASD by examining the literature on neurophysiological responses to sensory stimuli in individuals with ASD as well as structural and network organization using a variety of neuroimaging techniques. Many aspects of Ayres’ hypotheses about the nature of the disorder were found to be highly consistent with current literature on sensory processing in children with ASD but there are some discrepancies across various methodological techniques and ASD development. With additional characterization, neurophysiological profiles of sensory processing in ASD may serve as valuable biomarkers for diagnosis and monitoring of therapeutic interventions, such as SI therapy.
TL;DR: This review integrates previous literature on fatigue and proposes a unified schema aiming to clarify the fatigue taxonomy, and discusses evidence for the therapeutic potential of tES on cognitive fatigue in people with MS.
Abstract: Cognitive fatigue is one of the most frequent symptoms in multiple sclerosis (MS), associated with significant impairment in daily functioning and quality of life. Despite its clinical significance, progress in understanding and treating fatigue is still limited. This limitation is already caused by an inconsistent and heterogeneous terminology and assessment of fatigue. In this review, we integrate previous literature on fatigue and propose a unified schema aiming to clarify the fatigue taxonomy. With special focus on cognitive fatigue, we survey the significance of objective behavioral and electrophysiological fatigue parameters and discuss the controversial literature on the relationship between subjective and objective fatigue assessment. As MS-related cognitive fatigue drastically affects quality of life, the development of efficient therapeutic approaches for overcoming cognitive fatigue is of high clinical relevance. In this regard, the reliable and valid assessment of the individual fatigue level by objective parameters is essential for systematic treatment evaluation and optimization. Transcranial electrical stimulation (tES) may offer a unique opportunity to manipulate maladaptive neural activity underlying MS fatigue. Therefore, we discuss evidence for the therapeutic potential of tES on cognitive fatigue in people with MS.
TL;DR: The physiology and pathophysiology of postural balance that is essential to treat PI among PD patients is reviewed and contextualizing the contributing factors are discussed to assist the future research efforts that underpin posturographical and histopathological studies to measure PI in PD.
Abstract: Parkinson’s disease (PD) is a heterogeneous progressive neurodegenerative disorder, which typically affects older adults; it is predicted that by 2030 about 3% of the world population above 65 years of age is likely to be affected. At present, the diagnosis of PD is clinical, subjective, nonspecific, and often inadequate. There is a need to quantify the PD factors for an objective disease assessment. Among the various factors, postural instability (PI) is unresponsive to the existing treatment strategies resulting in morbidity. In this work, we review the physiology and pathophysiology of postural balance that is essential to treat PI among PD patients. Specifically, we discuss some of the reported factors for an early PI diagnosis, including age, nervous system lesions, genetic mutations, abnormal proprioception, impaired reflexes, and altered biomechanics. Though the contributing factors to PI have been identified, how their quantification to grade PI severity in a patient can help in treatment is not fully understood. By contextualizing the contributing factors, we aim to assist the future research efforts that underpin posturographical and histopathological studies to measure PI in PD. Once the pathology of PI is established, effective diagnostic tools and treatment strategies could be developed to curtail patient falls.
TL;DR: Empagliflozin may provide neuroprotection in the diabetic brain by protecting the NVU from abnormal remodeling in cortical gray and subcortical white matter in mice with type 2 diabetes mellitus.
Abstract: Type 2 diabetes is associated with diabetic cognopathy. Anti-hyperglycemic sodium glucose transporter 2 (SGLT2) inhibitors have shown promise in reducing cognitive impairment in mice with type 2 diabetes mellitus. We recently described marked ultrastructural (US) remodeling of the neurovascular unit (NVU) in type 2 diabetic db/db female mice. Herein, we tested whether the SGLT-2 inhibitor, empagliflozin (EMPA), protects the NVU from abnormal remodeling in cortical gray and subcortical white matter. Ten-week-old female wild-type and db/db mice were divided into lean controls (CKC, n = 3), untreated db/db (DBC, n = 3), and EMPA-treated db/db (DBE, n = 3). Empagliflozin was added to mouse chow to deliver 10 mg kg−1 day−1 and fed for ten weeks, initiated at 10 weeks of age. Brains from 20-week-old mice were immediately immersion fixed for transmission electron microscopic study. Compared to CKC, DBC exhibited US abnormalities characterized by mural endothelial cell tight and adherens junction attenuation and/or loss, pericyte attenuation and/or loss, basement membrane thickening, glia astrocyte activation with detachment and retraction from mural cells, microglia cell activation with aberrant mitochondria, and oligodendrocyte–myelin splitting, disarray, and axonal collapse. We conclude that these abnormalities in the NVU were prevented in DBE. Empagliflozin may provide neuroprotection in the diabetic brain.
TL;DR: The first comprehensive review on vigilance enhancement using both conventional and unconventional means is presented, and the effectiveness of unconventional means of enhancement in significant reduction of vigilance decrement compared to conventional means is revealed.
Abstract: This paper presents the first comprehensive review on vigilance enhancement using both conventional and unconventional means, and further discusses the resulting contradictory findings. It highlights the key differences observed between the research findings and argues that variations of the experimental protocol could be a significant contributing factor towards such contradictory results. Furthermore, the paper reveals the effectiveness of unconventional means of enhancement in significant reduction of vigilance decrement compared to conventional means. Meanwhile, a discussion on the challenges of enhancement techniques is presented, with several suggested recommendations and alternative strategies to maintain an adequate level of vigilance for the task at hand. Additionally, this review provides evidence in support of the use of unconventional means of enhancement on vigilance studies, regardless of their practical challenges.
TL;DR: Improve and propose a SincNet-based classifier, Sinc net-R, which consists of three convolutional layers, and three deep neural network (DNN) layers, which is used to test the classification accuracy and robustness by emotional EEG signals.
Abstract: Deep learning (DL) methods have been used increasingly widely, such as in the fields of speech and image recognition. However, how to design an appropriate DL model to accurately and efficiently classify electroencephalogram (EEG) signals is still a challenge, mainly because EEG signals are characterized by significant differences between two different subjects or vary over time within a single subject, non-stability, strong randomness, low signal-to-noise ratio. SincNet is an efficient classifier for speaker recognition, but it has some drawbacks in dealing with EEG signals classification. In this paper, we improve and propose a SincNet-based classifier, SincNet-R, which consists of three convolutional layers, and three deep neural network (DNN) layers. We then make use of SincNet-R to test the classification accuracy and robustness by emotional EEG signals. The comparable results with original SincNet model and other traditional classifiers such as CNN, LSTM and SVM, show that our proposed SincNet-R model has higher classification accuracy and better algorithm robustness.
TL;DR: The centrality of the embodiment disorder suggests the importance of considering the body image disturbance in people with AN as resulting from their difficulty in experiencing inner states and as a tool to build its own self, which may orient therapeutic interventions.
Abstract: Anorexia Nervosa (AN) is characterized by body image distortion. From a phenomenological perspective, body image disturbance has been associated with a more profound disturbance encompassing disorders of the way persons experience their own body. The aim of this study was to disentangle the complex dynamics that connect the experience of one’s own body and self-identity to the psychopathological features of AN by applying a network analysis. Fifty-seven patients with AN restrictive subtype and 27 with AN binge–purging subtype participated in the study. Eating Disorders Inventory-2 and Identity and Eating Disorders subscores, measuring the embodiment dimensions, were included in the network. Two of the main dimensions of embodiment—feeling extraneous from one’s own body and feeling oneself through objective measures—were the nodes with the highest strength together with interoceptive awareness (IA). IA was a node included in several pathways connecting embodiment dimensions with most of the AN psychopathological dimensions. The centrality of the embodiment disorder suggests the importance of considering the body image disturbance in people with AN as resulting from their difficulty in experiencing inner states and as a tool to build its own self. This assumption may orient therapeutic interventions.
TL;DR: In children and adolescents with Social Anxiety Disorder, it is necessary to evaluate firstly the presence of peer victimization experiences and subsequently, therapeutics programs targeted to elaborate these experiences and to reduce the anticipatory anxiety and the avoidance that characterized these children and adolescence.
Abstract: Background: In the literature, several studies have proposed that children and adolescents with social anxiety had experienced previously victimization from peers and siblings. The aim of this review was to contribute to the updating of recent findings about the relationship between peer victimization and onset of social anxiety in children and adolescents. Methods: A selective review of literature published between 2011 and 2018 on Social Anxiety Disorder in children and adolescents that experienced peer victimization during childhood and adolescence. Results: Seventeen studies are included. All studies showed that peer victimization is positively correlated to the presence of social anxiety. Moreover, the perpetration of peer victimization may contribute to the maintenance and the exacerbation of social anxiety symptoms. Conclusions: In children and adolescents with Social Anxiety Disorder, it is necessary to evaluate firstly the presence of peer victimization experiences. Subsequently, therapeutics programs targeted to elaborate these experiences and to reduce the anticipatory anxiety and the avoidance that characterized these children and adolescents can be proposed.
TL;DR: Naringenin-loaded chitosan nanoparticles (NAR NPs) showed enhanced neuroprotective ability and antioxidant effect against 6-OHDA-induced neurotoxicity in SH-SY5Y cells, however, animal studies are necessary to establish the potential of NARNPs to be an effective carrier for the treatment of PD for nose-to-brain delivery.
Abstract: Parkinson’s disease (PD) is a neurodegenerative disorder resulting in a decreased nigrostriatal availability of dopamine. Oxidative stress is one factor contributing to PD. Naringenin (NAR), a flavonoid, is a potent antioxidant shown to be beneficial in experimental PD. The clinical development of NAR has been hampered due to its low bioavailability resulting from gastrointestinal degradation, inefficient permeability, and low aqueous solubility. The objective of the present research was to formulate and characterize naringenin-loaded chitosan nanoparticles (NAR NPs) for nose-to-brain delivery. The cellular uptake, cytotoxicity, and neuroprotective effects of NAR NPs were determined using the SH-SY5Y cell line in vitro. NAR NPs were prepared using the ionic gelation method and characterized by zetasizer, transmission electron microscopy (TEM), and field emission microscopy (FESEM). The average particle size, polydispersity index (PDI), zeta potential, entrapment efficiency, and 24 h in vitro release profile were 87.6 ± 8.47 nm, 0.31 ± 0.04, 15.36 ± 2.05 mV, 91.12 ± 2.99%, and 54.80 ± 4.22%, respectively. The percentage NAR permeation through nasal mucosa from NPs was found to be 67.90 ± 0.72%. Cellular uptake of prepared NPs was confirmed by fluorescence microscopy. Neuroprotective activity of NAR NPs was evaluated through viability assays and by estimating reactive oxygen species (ROS) levels. NAR NPs showed enhanced neuroprotective ability and antioxidant effect against 6-OHDA-induced neurotoxicity in SH-SY5Y cells. However, animal studies are necessary to establish the potential of NAR NPs to be an effective carrier for the treatment of PD for nose-to-brain delivery.
TL;DR: This review describes the paramount discoveries obtained through the application of different α-syn models of PD in C. elegans and highlights their established utility and respective promise to successfully uncover new conserved genetic modifiers, functional mechanisms, therapeutic targets and molecular leads for PD with the potential to translate to humans.
Abstract: Parkinson’s Disease (PD) is the second-most common neurodegenerative disease in the world, yet the fundamental and underlying causes of the disease are largely unknown, and treatments remain sparse and impotent. Several biological systems have been employed to model the disease but the nematode roundworm Caenorhabditis elegans (C. elegans) shows unique promise among these to disinter the elusive factors that may prevent, halt, and/or reverse PD phenotypes. Some of the most salient of these C. elegans models of PD are those that position the misfolding-prone protein alpha-synuclein (α-syn), a hallmark pathological component of PD, as the primary target for scientific interrogation. By transgenic expression of human α-syn in different tissues, including dopamine neurons and muscle cells, the primary cellular phenotypes of PD in humans have been recapitulated in these C. elegans models and have already uncovered multifarious genetic factors and chemical compounds that attenuate dopaminergic neurodegeneration. This review describes the paramount discoveries obtained through the application of different α-syn models of PD in C. elegans and highlights their established utility and respective promise to successfully uncover new conserved genetic modifiers, functional mechanisms, therapeutic targets and molecular leads for PD with the potential to translate to humans.
TL;DR: An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched.
Abstract: Electroencephalography (EEG) signals are frequently contaminated with unwanted electrooculographic (EOG) artifacts. Blinks and eye movements generate large amplitude peaks that corrupt EEG measurements. Independent component analysis (ICA) has been used extensively in manual and automatic methods to remove artifacts. By decomposing the signals into neural and artifactual components and artifact components can be eliminated before signal reconstruction. Unfortunately, removing entire components may result in losing important neural information present in the component and eventually may distort the spectral characteristics of the reconstructed signals. An alternative approach is to correct artifacts within the independent components instead of rejecting the entire component, for which wavelet transform based decomposition methods have been used with good results. An improved, fully automatic wavelet-based component correction method is presented for EOG artifact removal that corrects EOG components selectively, i.e., within EOG activity regions only, leaving other parts of the component untouched. In addition, the method does not rely on reference EOG channels. The results show that the proposed method outperforms other component rejection and wavelet-based EOG removal methods in its accuracy both in the time and the spectral domain. The proposed new method represents an important step towards the development of accurate, reliable and automatic EOG artifact removal methods.
TL;DR: Alexithymic features were significantly more prevalent in pathological gamblers both at the community and clinical levels, increased symptom severity, and showed interactive mechanisms with personality, psychiatric, and cognitive factors.
Abstract: Among the factors that are thought to underlie gambling problems, alexithymia has been recognized to contribute to their development. For the first time, we reviewed the literature on the relationship between alexithymia and gambling. A systematic search of literature was run in the major reference databases including PubMed, Cochrane Database for Systematic Review, PsycINFO, Web of Science, Scopus until April 2019. The search produced 182 articles that produced 20 papers included in the review. Fourteen studies were conducted with community samples of pathological gamblers while six studies with clinical samples of disordered gamblers. All studies assessed alexithymia with the Toronto Alexithymia Scale while gambling problems were assessed mostly with the South Oaks Gambling Screen. Alexithymic features were significantly more prevalent in pathological gamblers both at the community and clinical levels, increased symptom severity, and showed interactive mechanisms with personality, psychiatric, and cognitive factors. Alexithymia is likely to associate with gambling as a coping behavior to increase emotional arousal and avoid negative emotions, according to the affect dysregulation model. Further studies are needed to widen the knowledge on this association.