Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8-13) Hz, beta (13-30) Hz and gamma (30-49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.
Pathophysiology of postpartum depression (PPD) has been associated with many factors, such as neuroendocrine, neuroinflammation and neurotransmitter changes. Fish oil (FO) improves PPD both in humans and animals. However, little is known with regards to its pharmacology on a PPD-like rat model. Hence, the current study aimed at investigating the effects of FO on a PPD-like rat model. Female rats were induced with PPD-like symptoms and then randomly divided into six groups (n = 6) for two experimental protocols. Protocol 1 consisted of PPD-like rats (2 mL distilled water), PPD-like + FO (9 g/kg/d) and PPD-like + Fluoxetine (FLX) (15 mg/kg/d) groups of rats, whereas Protocol 2 consisted of PPD-like rats (2 mL distilled water) + PCPA (p-chlorophenylalanine) 150 mg/kg, PPD-like + FO (9 g/kg/d) + PCPA 150 mg/kg and PPD-like + FLX (15 mg/d) + PCPA 150 mg/kg groups of rats, respectively. All treatments were administered orally for 10 days postpartum, except PCPA, which was given intraperitoneally. Prior to euthanasia, the antidepressant-like effect of the FO was evaluated using the forced swimming test (FST) and open field test (OFT) on day 10 postpartum. Biochemical analysis of serotonin, serotonin metabolite and serotonin turnover from their prefrontal cortex and hippocampus were also measured. The results showed that FO decreased immobility time and increased swimming time significantly, but not climbing time in FST. Further, it also decreased serotonin metabolite and turnover significantly in the hippocampus of the PPD-like rats. In contrast, administration with PCPA reversed all the outcomes. The antidepressant-like effects of FO were found to be similar with that of FLX. Thus, it can be concluded that FO exerts its antidepressant-like effects in PPD-like rats through modulation of serotonergic system.
Covert cognition in patients with disorders of consciousness represents a real diagnostic conundrum for clinicians. In this meta-analysis, our main objective was to identify clinical and demographic variables that are more likely to be associated with responding to an active paradigm. Among 2018 citations found on PubMed, 60 observational studies were found relevant. Based on the QUADAS-2, 49 studies were considered. Data from 25 publications were extracted and included in the meta-analysis. Most of these studies used electrophysiology as well as counting tasks or mental imagery. According to our statistical analysis, patients clinically diagnosed as being in a vegetative state and in a minimally conscious state minus (MCS-) show similar likelihood in responding to active paradigm and responders are most likely suffering from a traumatic brain injury. In the future, multi-centric studies should be performed in order to increase sample size, with similar methodologies and include structural and functional neuroimaging in order to identify cerebral markers related to such a challenging diagnosis.
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.
The concept of wholeness or oneness refers to not only humans, but also all of creation. Similarly, consciousness may not wholly exist inside the human brain. One consciousness could permeate the whole universe as limitless energy; thus, human consciousness can be regarded as limited or partial in character. According to the limited consciousness concept, humans perceive projected waves or wave-vortices as a waveless item. Therefore, human limited consciousness collapses the wave function or energy of particles; accordingly, we are only able to perceive them as particles. With this "limited concept", the wave-vortex or wave movement comes into review, which also seems to have a limited concept, i.e., the limited projected wave concept. Notably, this wave-vortex seems to embrace photonic light, as well as electricity and anything in between them, which gives a sense of dimension to our brain. These elements of limited projected wave-vortex and limitless energy (consciousness) may coexist inside our brain as electric (directional pilot wave) and quantum (diffused oneness of waves) brainwaves, respectively, with both of them giving rise to one brain field. Abnormality in either the electrical or the quantum field or their fusion may lead to abnormal brain function.
Tinnitus is the perception of a sound without an external source, often associated with adverse psychological and emotional effects leading to impaired quality of life (QoL). The present study investigated QoL and psychological distress in tinnitus patients and analysed the effects of associated comorbidities. Tonal and speech audiometry, tinnitus assessment, and clinical interviews were obtained from 122 Portuguese individuals (aged from 55 to 75). Portuguese versions of the Brief Symptoms Inventory (BSI), the Medical Outcomes Study Short Form Health Survey (MOS SF-36) and Tinnitus Handicap Inventory (THI) were used to evaluate psychological distress, health-related QoL, social difficulties and tinnitus severity. The presence of tinnitus was significantly associated with hearing loss. The increases in tinnitus severity were associated with decreases in QoL, particularly regarding MOS SF-36 subscales "perception of health", "social functioning", and "mental health". Regarding BSI, patients with greater tinnitus severity had more severe psychopathology symptoms, measured with scales "Obsessive-compulsive", "Depression", "Anxiety", "Hostility" and "Phobic Anxiety". Our study supports the notion of the negative impact of increased tinnitus severity on QoL and psychological distress in older adults. Presented data strengthen the importance of a multidisciplinary approach to tinnitus assessment and treatment.
The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.
Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment. The EEG was recorded using 20 electrodes and hemodynamic responses were recorded using 32 optodes positioned over the motor cortex areas. The event-related desynchronization (ERD) feature was extracted from the EEG signal in the alpha band (8-11) Hz, and the concentration change of the oxy-hemoglobin (oxyHb) was evaluated from the hemodynamics response. During the motor execution of the ankle joint movements, a decrease in the alpha (8-11) Hz amplitude (desynchronization) was found to be correlated with an increase of the oxyHb (r = -0.64061, p < 0.00001) observed on the Cz electrode and the average of the fNIRS channels (ch28, ch25, ch32, ch35) close to the foot area representation. Then, the correlated channels in both modalities were used for ankle joint movement classification. The result demonstrates that the integrated modality based on the correlated channels provides a substantial enhancement in ankle joint classification accuracy of 93.01 ± 5.60% (p < 0.01) compared with single modality. These results highlight the potential of the bimodal fNIR-EEG approach for the development of future BCI for lower limb rehabilitation.
Glial cell line-derived neurotrophic factor (GDNF) was initially described as important for dopaminergic neuronal survival and is involved in many other essential functions in the central nervous system. Characterization of GDNF phenotype in mammals is well described; however, studies in non-mammalian vertebrate models are scarce. Here, we characterized the anatomical distribution of gdnf-expressing cells in adult zebrafish brain by means of combined in situ hybridization (ISH) and immunohistochemistry. Our results revealed that gdnf was widely dispersed in the brain. gdnf transcripts were co-localized with radial glial cells along the ventricular area of the telencephalon and in the hypothalamus. Interestingly, Sox2 positive cells expressed gdnf in the neuronal layer but not in the ventricular zone of the telencephalon. A subset of GABAergic precursor cells labeled with dlx6a-1.4kbdlx5a/6a: green fluorescence protein (GFP) in the pallium, parvocellular preoptic nucleus, and the anterior and dorsal zones of the periventricular hypothalamus also showed expression with gdnf mRNA. In addition, gdnf signals were detected in subsets of dopaminergic neurons, including those in the ventral diencephalon, similar to what is seen in mammalian brain. Our work extends our knowledge of gdnf action sites and suggests a potential role for gdnf in adult brain neurogenesis and regeneration.
Magnetic resonance imaging (MRI) exams may cause patients to feel anxious before or during the scan, which affects the scanning outcome and leads to motion artifacts. Adequate preparation can effectively alleviate patients' anxiety before the scan. We aimed to assess the effect of different preparation methods on MRI-induced anxiety: We conducted a prospective randomized study on MRI patients between March and May 2022. We divided 30 patients into two groups: the control group, which received routine preparation (RP), and the experimental group, which received video preparation (VP). We used the State-Trait Anxiety Inventory (STAI) to measure anxiety levels before and after the interventions. We assessed patients' self-satisfaction after the scan: After preparation, VP (STAI mean = 10.7500) and RP (STAI mean = 12.7857), we observed a significant association between the pre- and post-STAI results in VP (p = 0.025). The effects of both methods in decreasing anxiety were more significant for first-timers (p = 0.009 in RP/0.014 in VP). We noted high satisfaction levels for both forms of preparation. The VP technique was superior in reducing patient anxiety, especially in first-time MRI patients. Hence, VP techniques can be used in different clinical settings to reduce anxiety and facilitate patients' understanding of the instructions given.
Asymptomatic (or "silent") manifestations of cerebral small vessel disease (CSVD) are widely recognized through incidental findings of white matter hyperintensities (WMHs) as a result of magnetic resonance imaging (MRI). This study aims to examine the potential associations of surrogate markers for the evaluation of white matter integrity in CSVD among asymptomatic individuals through a battery of profiling involving QRISK2 cardiocerebrovascular risk prediction, neuroimaging, neurocognitive evaluation, and microparticles (MPs) titers. Sixty asymptomatic subjects (mean age: 39.83 ± 11.50 years) with low to moderate QRISK2 scores were recruited and underwent neurocognitive evaluation for memory and cognitive performance, peripheral venous blood collection for enumeration of selected MPs subpopulations, and 3T MRI brain scan with specific diffusion MRI (dMRI) sequences inclusive of diffusion tensor imaging (DTI). WMHs were detected in 20 subjects (33%). Older subjects (mean age: 46.00 ± 12.00 years) had higher WMHs prevalence, associated with higher QRISK2 score and reduced processing speed. They also had significantly higher mean percentage of platelet (CD62P)- and leukocyte (CD62L)-derived MPs. No association was found between reduced white matter integrity-especially at the left superior longitudinal fasciculus (LSLF)-with age and neurocognitive function; however, LSLF was associated with higher QRISK2 score, total MPs, and CD62L- and endothelial cell-derived MPs (CD146). Therefore, this study establishes these multimodal associations as potential surrogate markers for "silent" CSVD manifestations in the well-characterized cardiocerebrovascular demographic of relatively young, neurologically asymptomatic adults. Furthermore, to the best of our knowledge, this study is the first to exhibit elevated MP counts in asymptomatic CSVD (i.e., CD62P and CD62L), which warrants further delineation.
Parkinson's disease (PD) is a complex degenerative brain disease that affects nerve cells in the brain responsible for body movement. Machine learning is widely used to track the progression of PD in its early stages by predicting unified Parkinson's disease rating scale (UPDRS) scores. In this paper, we aim to develop a new method for PD diagnosis with the aid of supervised and unsupervised learning techniques. Our method is developed using the Laplacian score, Gaussian process regression (GPR) and self-organizing maps (SOM). SOM is used to segment the data to handle large PD datasets. The models are then constructed using GPR for the prediction of the UPDRS scores. To select the important features in the PD dataset, we use the Laplacian score in the method. We evaluate the developed approach on a PD dataset including a set of speech signals. The method was evaluated through root-mean-square error (RMSE) and adjusted R-squared (adjusted R²). Our findings reveal that the proposed method is efficient in the prediction of UPDRS scores through a set of speech signals (dysphonia measures). The method evaluation showed that SOM combined with the Laplacian score and Gaussian process regression with the exponential kernel provides the best results for R-squared (Motor-UPDRS = 0.9489; Total-UPDRS = 0.9516) and RMSE (Motor-UPDRS = 0.5144; Total-UPDRS = 0.5105) in predicting UPDRS compared with the other kernels in Gaussian process regression.
Prognosis of prolonged Disorders of Consciousness (pDoC) is influenced by patients' clinical diagnosis and Coma Recovery Scale-Revised (CRS-R) total score. We compared the prognostic accuracy of a novel Consciousness Domain Index (CDI) with that of clinical diagnosis and CRS-R total score, for recovery of full consciousness at 6-, 12-, and 24-months post-injury. The CDI was obtained by a combination of the six CRS-R subscales via an unsupervised machine learning technique. We retrospectively analyzed data on 143 patients with pDoC (75 in Minimally Conscious State; 102 males; median age = 53 years; IQR = 35; time post-injury = 1-3 months) due to different etiologies enrolled in an International Brain Injury Association Disorders of Consciousness Special Interest Group (IBIA DoC-SIG) multicenter longitudinal study. Univariate and multivariate analyses were utilized to assess the association between outcomes and the CDI, compared to clinical diagnosis and CRS-R. The CDI, the clinical diagnosis, and the CRS-R total score were significantly associated with a good outcome at 6, 12 and 24 months. The CDI showed the highest univariate prediction accuracy and sensitivity, and regression models including the CDI provided the highest values of explained variance. A combined scoring system of the CRS-R subscales by unsupervised machine learning may improve clinical ability to predict recovery of consciousness in patients with pDoC.
Aducanumab, a human monoclonal antibody, was approved in June of 2021 as the first disease-modifying treatment for Alzheimer's disease by the United States Food and Drug Administration (U.S. FDA). A substantial proportion of patients with Alzheimer's disease live in low- and middle-income countries (LMICs), and the debilitating effects of this disease exerts burdens on patients and caregivers in addition to the significant economic strains many nations bear. While the advantages of a disease-modifying therapy are clear in delaying the progression of disease to improve patient outcomes, aducanumab's approval by the U.S. FDA was met with controversy for a plethora of reasons. This paper will provide precursory insights into aducanumab's role, appropriateness, and cost-effectiveness in low- and middle-income countries. We extend some of the controversies associated with aducanumab, including the contradicting evidence from the two trials (EMERGE and ENGAGE) and the resources required to deliver the treatment safely and effectively to patients, among other key considerations.
Murraya koenigii leaves contain mahanimbine, a carbazole alkaloid, reported with improving cholinergic neuronal transmission and reducing neuroinflammation in the CNS. The current research investigated the effects of mahanimbine on age-related memory deficits, oxidative stress, cholinergic dysfunction, amyloid formation, and neuroinflammation in aged mice (16 months old). Mahanimbine was administered (1 and 2 mg/kg, p.o.) daily to groups of aged mice for 30 days. The Morris water maze (MWM) task was performed to study spatial learning (escape latency (EL) and swimming distance (SD)) and memory (probe test). The levels of malondialdehyde (MDA), glutathione (GSH), acetylcholine (ACh), acetylcholinesterase (AChE), β-amyloid (Aβ1-40 and Aβ1-42), β-secretase (BACE-1), as well as neuroinflammation markers (total cyclooxygenase (COX) and COX-2 expression), were measured from the isolated brain. Mahanimbine reduced the EL time and SD in the MWM test. From the probe trial, the mahanimbine-treated group spent more time in the targeted quadrant related to the age-matched control, which indicated the enhancement of memory retention. From the biochemical tests, the treatment decreased MDA, AChE, Aβ1-40, and Aβ1-42, BACE-1, total COX activity, and COX-2 expression. It also raised the brain GSH and ACh levels in aged mice compared to age-matched control. These results have supported the reversal of memory dysfunctions by mahanimbine in aged mice and hypothesized that it could be a potential target to treat age-related neurodegenerative disease.