Cross-frequency coupling (CFC) mechanisms play a central role in brain activity. Pathophysiological mechanisms leading to many brain disorders, such as Alzheimer's disease (AD), may produce unique patterns of brain activity detectable by electroencephalography (EEG). Identifying biomarkers for AD diagnosis is also an ambition among research teams working in Down syndrome (DS), given the increased susceptibility of people with DS to develop early-onset AD (DS-AD). Here, we review accumulating evidence that altered theta-gamma phase-amplitude coupling (PAC) may be one of the earliest EEG signatures of AD, and therefore may serve as an adjuvant tool for detecting cognitive decline in DS-AD. We suggest that this field of research could potentially provide clues to the biophysical mechanisms underlying cognitive dysfunction in DS-AD and generate opportunities for identifying EEG-based biomarkers with diagnostic and prognostic utility in DS-AD.

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be found in this direction, there is still room for improvement both from a decoding, hardware, and subject-motor learning perspective. Here we show, in a series of experiments with non-disabled participants, a novel decoding and training paradigm allowing naïve participants to use their auricular muscles (AM) to control two degrees of freedom with a virtual cursor. AMs are particularly interesting because they are vestigial muscles and are often preserved after neurological diseases. Our method relies on the use of surface electromyographic records and the use of contraction levels of both AMs to modulate the velocity and direction of a cursor in a two-dimensional paradigm. We used a locking mechanism to fix the current position of each axis separately to enable the user to stop the cursor at a certain location. A five-session training procedure (20-30 min per session) with a 2D center-out task was performed by five volunteers. All participants increased their success rate (Initial: 52.78 ± 5.56%; Final: 72.22 ± 6.67%; median ± median absolute deviation) and their trajectory performances throughout the training. We implemented a dual task with visual distractors to assess the mental challenge of controlling while executing another task; our results suggest that the participants could perform the task in cognitively demanding conditions (success rate of 66.67 ± 5.56%). Finally, using the Nasa Task Load Index questionnaire, we found that participants reported lower mental demand and effort in the last two sessions. To summarize, all subjects could learn to control the movement of a cursor with two degrees of freedom using their AM, with a low impact on the cognitive load. Our study is a first step in developing AM-based decoders for HMIs for people with motor disabilities, such as spinal cord injury.

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality–based testing and training and the proposed pipeline.

Purpose One of the main causes of long-term prosthetic abandonment is the lack of ownership over the prosthesis, which was caused mainly by the absence of sensory information regarding the lost limb. The period where the patient learns how to interact with a prosthetic device is critical in rehabilitation. This ideally happens within the first months after amputation, which is also a period associated with the consolidation of brain changes. Different studies have shown that the introduction of feedback mechanisms can be crucial to bypass the lack of sensorial information. To develop a biofeedback system for the rehabilitation of transfemoral amputees – controlled via electromyographic (EMG) activity from the leg muscles – that can provide real-time visual and/or vibratory feedback for the user. Design/methodology/approach The system uses surface EMG to control two feedback mechanisms, which are the knee joint of a prosthetic leg of a humanoid avatar in a virtual reality (VR) environment (visual feedback) and a matrix of 16 vibrotactile actuators placed in the back of the user (vibratory feedback). Data acquisition was inside a Faraday Cage using an OpenEphys® acquisition board for the surface EMG recordings. The tasks were performed on able-bodied participants, with no amputation, and for this, the dominant leg of the user was immobilized using an orthopedic boot fixed on the chair, allowing only isometric contractions of target muscles, according to the Surface EMG for Non-Invasive Assessment of Muscles (SENIAM) standard. The authors test the effectiveness of combining vibratory and visual feedback and how task difficulty affects overall performance. Findings The authors' results show no negative interference combining both feedback modalities and that performance peaked at the intermediate difficulty. These results provide powerful insights of what can be accomplished with the population of amputee people. By using this biofeedback system, the authors expect to engage another sensory modality in the process of spatial representation of a virtual leg, bypassing the lack of information associated with the disruption of afferent pathways following amputation. Research limitations/implications The authors developed a showcase with a new protocol and feedback mechanisms showing the protocol's safety, efficiency and reliability. However, since this system is designed for patients with leg amputation, the full extent of the effects of the biofeedback training can only be assessed after the evaluation with the amputees, and the results obtained so far establish a safe and operational protocol to accomplish this. Practical implications In this study, the authors proposed a new biofeedback device intended to be used in the preprosthetic rehabilitation phase for people with transfemoral amputation. With this new system, the authors propose a mechanism to bypass the lack of sensory information from a virtual prosthesis and help to assimilate visual and vibrotactile stimuli as a cue for movement representation. Social implications With this new system, the authors propose a mechanism to bypass the lack of sensory information from a virtual prosthesis and help to assimilate visual and vibrotactile stimuli as a cue for movement representation. Originality/value The authors' results show that all users were capable of recognizing both feedback modalities, both separate and combined, being able to respond accordingly throughout the tasks. The authors also show that for a one-session protocol, the last difficulty level imposed a greater challenge for most users, explained by the significant drop in performance disregarding the feedback modality. Lastly, the authors believe this paradigm can provide a better process for the embodiment of prosthetic devices, fulfilling the lack of sensory information for the users.

Mounting evidence implicates dysfunctional GABAAR-mediated neurotransmission as one of the underlying causes of learning and memory deficits observed in the Ts65Dn mouse model of Down syndrome (DS). The specific origin and nature of such dysfunction is still under investigation, which is an issue with practical consequences to preclinical and clinical research, as well as to the care of individuals with DS and anxiety disorder or those experiencing seizures in emergency room settings. Here, we investigated the effects of GABAAR positive allosteric modulation (PAM) by diazepam on brain activity, synaptic plasticity, and behavior in Ts65Dn mice. We found Ts65Dn mice to be less sensitive to diazepam, as assessed by electroencephalography, long-term potentiation, and elevated plus-maze. Still, diazepam pre-treatment displayed typical effectiveness in reducing susceptibility and severity to picrotoxin-induced seizures in Ts65Dn mice. These findings fill an important gap in the understanding of GABAergic function in a key model of DS.

In recent years, new and important perspectives were introduced in the field of neuroimaging with the emergence of the connectionist approach. In this new context, it is important to know not only which brain areas are activated by a particular stimulus but, mainly, how these areas are structurally and functionally connected, distributed, and organized in relation to other areas. Additionally, the arrangement of the network elements, i.e., its topology, and the dynamics they give rise to are also important. This new approach is called connectomics. It brings together a series of techniques and methodologies capable of systematizing, from the different types of signals and images of the nervous system, how neuronal units to brain areas are connected. Through this approach, the different patterns of connectivity can be graphically and mathematically represented by the so-called connectomes. The connectome uses quantitative metrics to evaluate structural and functional information from images of neural tracts and pathways or signals from the metabolic and/or electrophysiologic activity of cell populations or brain areas. Besides, with adequate treatment of this information, it is also possible to infer causal relationships. In this way, structural and functional evaluations are complementary descriptions which, together, represent the anatomic and physiologic neural properties, establishing a new paradigm for understanding how the brain functions by looking at brain connections. Here, we highlight five critical elements of a network that allows an integrative analysis, focusing mainly on a functional description. These elements include; (i) the properties of its nodes; (ii) the metrics for connectivity and coupling between nodes; (iii) the network topologies; (iv) the network dynamics and (v) the interconnections between different domains and scales of network representations.

Modulation of brain activity is one of the main mechanisms capable of demonstrating the synchronization dynamics of neural oscillations. In epilepsy, modulation is a key concept since seizures essentially result from neural hypersynchronization and hyperexcitability. In this study, we have introduced a time-dependent index based on the Kullback-Leibler divergence to quantify the effects of phase and frequency modulations of neural oscillations in neonatal mice exhibiting epileptiform activity induced by Zika virus (ZIKV) infection. Through this index, we demonstrate that fast oscillations (gamma and beta 2) are the more susceptible modulated rhythms in terms of phase, during seizures, whereas slow waves (delta and theta) mainly undergo changes in frequency. The index also allowed detection of specific patterns associated with the interdependent modulation of phase and frequency in neural activity. Furthermore, by comparing ZIKV modulations with the general computational model Epileptors , we verify different signatures related to the brain rhythms modulation in phase and frequency. These findings instigate new studies on the effects of ZIKV infection on neuronal networks from electrophysiological activities, and how different mechanisms can trigger epilepsy.

Patients with Parkinson’s disease (PD) manifest nonmotor and motor symptoms. Autonomic cardiovascular dysregulation is a common nonmotor manifestation associated with increased morbimortality. Conventional clinical treatment alleviates motor signs but does not change disease progression and fails in handling nonmotor features. Nutrition is a key modifiable determinant of chronic disease. This study aimed to assess the effects of propolis on cardiological features, heart rate (HR) and heart rate variability (HRV) and on nigrostriatal dopaminergic damage, detected by tyrosine hydroxylase (TH) immunoreactivity, in the 6-hydroxydopamine (6-OHDA) rat model of PD. Male Wistar rats were injected bilaterally with 6-OHDA or saline into the striatum and were treated with propolis or water for 40 days. Autonomic function was assessed by time domain parameters (standard deviation of all normal-to-normal intervals (SDNN) and square root of the mean of the squared differences between adjacent normal RR intervals (RMSSD)) of HRV calculated from electrocardiogram recordings. Reductions in HR (p = 1.47 × 10−19), SDNN (p = 3.42 × 10−10) and RMSSD (p = 8.2 × 10−6) detected in parkinsonian rats were reverted by propolis. Propolis attenuated neuronal loss in the substantia nigra (p = 5.66 × 10−15) and reduced striatal fiber degeneration (p = 7.4 × 10−5) in 6-OHDA-injured rats, which also showed significant weight gain (p = 1.07 × 10−5) in comparison to 6-OHDA-lesioned counterparts. Propolis confers cardioprotection and neuroprotection in the 6-OHDA rat model of PD.

Pre-eclampsia (PE) affects approximately 2 to 8% of pregnant women, causing blood pressure above 140 × 90 mmHg and proteinuria, normally after the 20th gestation week. If unsuccessfully treated, PE can lead to self-limited seizures (Eclampsia) that could eventually result in death of the mother and her fetus. The present study reports an experimental model of preeclampsia hypertension in pregnant (HP) and non-pregnant (H) Wistar rats by partially clamping one of their renal arteries. Pregnant (P) and non-pregnant (C) controls were provided. Differently from controls (C and P), H and HP animals presented a steady rise in BP two weeks after renal artery clamping. Injection of pentylenetetrazol (PTZ) induced behavioral and electroencephalographic seizures in all groups, which were increased in number, duration, amplitude and power accompanied by decreased latency in HP animals (p < 0.05). Consistent results were obtained in in vitro experimentation. Immunohistochemistry of hippocampus tissue in HP animals showed decreased density of neurons nuclei in CA1, CA3 and Hilus and increased density of astrocytes in CA1, CA3 and gyrus (p < 0.05). The present findings show that the clamping of one renal arteries to 0.15 mm and PTZ administration were able to induce signs similar to human PE in pregnant Wistar rats.

Infection by Zika virus triggers epileptic seizures in newborn mice and leads to viral persistence in the brain and behavioral deficits in adulthood.

The myocardial infarction, known as heart attack, is the ultimate result of a prolonged/untreated cardiac ischemia. The accurate segmentation of the myocardial infarction or ischemia in images obtained from diversified sources, such as Magnetic Resonance Images or Echocardiograph, is worthwhile for the medical area or the animal experimentation. An alternative image source for ischemia/infarction segmentation is the photo, which can depict the actual heart image. This work presents a method for ischemia segmentation in rat heart photos. The method applicability was tested in pictures of human hearts available in public databases from the Internet. At first, heart images were separated from the background using GrabCut method. Secondly, the segmentation of the cardiac ischemia region was performed by using Fuzzy Clustering method. Finally, a sequence of image processing (including morphological operations to remove small components and to fill the holes) was performed to obtain the final segmentation image. All resulting images were compared with the corresponding images containing contours of cardiac ischemia drawn manually by specialists. The mean accuracy was 83.24% ± 04.16%. As for the intrinsically human errors (tracing error between two specialists: 18.94% ± 05.30%), the average accuracy is within the inter-operator variability. As for the human heart pictures obtained from public libraries, the algorithm segmented the infarction areas correctly. The results show that the algorithm effectively helps the visualization of the cardiac ischemia/infarction region and has the potential to be applied to heart images of animals or humans, representing a versatile tool to assist advances in cardiomyopathology studies.

A causal relationship between congenital Zika virus (ZIKV) exposure and microcephaly and other neurological disorders have been established, but long-term consequences of infection are still unknown. We evaluated acute and late neuropathological and behavioral consequences of ZIKV infection in a neonatal immunocompetent mouse model. ZIKV showed brain tropism, causing post-natal microcephaly and several behavioral dysfunctions. During the acute phase of infection, mice developed very frequent epileptic seizures, which are consistently reduced by TNF-α neutralization. Although adult animals recover from seizures, they become more susceptible to chemically-induced crises. Intriguingly, the virus remained actively replicating in adult animals, which show persistent necrosis and calcifications in the mice brain. Altogether the results reveal late consequences of neonatal ZIKV exposure and suggest the early inhibition of neuroinflammation as a potential treatment.

Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing pathologies like hypertrophic cardiomyopathy, aneurysms, abnormal movement and structural faults. This work presents a hybrid neural network system to segment interventricular septum in echocardiographic images of parasternal longitudinal view. The hybrid system developed here consist of a Self-Organizing Map and a Multilayer Perceptron (MLP) neural network. The approach has two phases: clustering and classification. First, the Self-Organizing Map clusters image patches that are previously labeled as Septum and Non-septum. Later, an MLP is trained with information generated by the map. The MLP is then employed to classify patches of a new image resulting in a mask that indicates the probable septum regions. To validate the results, we did a semi-automatic extraction of septum thickness. The average error between the septum thicknesses obtained by the algorithm and the one manually traced was 0.5477mm ± 0.5277mm. Future recommendations are presented to improve the hybrid system performance to get more accurate results.