Search Results (18)

Insulin-like growth factor I (IGF-I) is a neurotrophic factor that regulates neurogenesis, synaptogenesis, and neuronal survival. It also enhances neuronal activity and facilitates synaptic plasticity. Additionally, IGF-I plays a critical role in the regulation of metabolism in mammals. Emerging evidence indicates that IGF-I modulates sleep architecture. The circadian integration of metabolic and neuronal systems serves to optimize energy utilization across the light/dark cycle. Current data suggest that IGF-I may be a key mediator of this integration, promoting brain activity during wakefulness, a state that coincides with increased metabolic demand. In this review, we summarize recent findings on the interplay between metabolism, IGF-I, and brain activity. Full article

Electrochemical disinfection in dentistry using boron-doped diamond (BDD) electrodes bears the potential risk of disturbing vital functions. Applying different arrays of BDD electrodes and an electrotome as reference, it was the goal of this animal study to compare their effects on an electrocorticogram (ECoG) and electrocardiogram (ECG). Following the trepanation of teeth in rats, the electrodes and electrotome were applied in a randomized manner while recording ECoG and ECG. The recordings were classified according to an electrophysiological significance score based on involvement, extent of disruption and duration. The scores obtained were compared by means of ANOVA followed by Dunn’s multiple comparisons test (α = 0.05). Voltage type and electrode design had a significant influence on the detectable electrophysiological effects. The results seen with BDD electrodes ranged from no detectable electrophysiological effects to a pronounced effect. The application of the electrotome induced the most pronounced effects. Given that electrotomes are safe medical devices, despite evoking greater disturbance compared to BDD electrodes, regardless of their design, electrochemical disinfection may be considered a safe procedure. Full article

This paper developed a comprehensive magnetic resonance imaging (MRI)-compatible electrophysiological (EP) acquisition system, which can acquire various physiological electrical signals, including electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG) and electrocorticogram (ECoG), and EP recording combined with multimodal stimulation. The system is designed to be compatible with the 7-Tesla (7T) ultra-high field MRI environment, providing convenience for neuroscience and physiological research. To achieve MRI compatibility, the device uses magnetically compatible materials and shielding measures on the hardware and algorithm processing on the software side. Different filtering algorithms are adopted for different signals to suppress all kinds of interference in the MRI environment. The system can allow input signals up to ±0.225 V and channels up to 256. The equipment has been tested and proven to be able to collect a variety of physiological electrical signals effectively. When scanned under the condition of a 7T high-intensity magnetic field, the system does not generate obvious heating and can meet the safety requirements of MRI and EEG acquisition requirements. Moreover, an algorithm is designed and improved to efficiently and automatically remove the gradient artifact (GA) noise generated by MRI, which is a thousand-fold gradient artifact. Overall, this work proposes a complete, portable, MRI-compatible system that can collect a variety of physiological electrical signals and integrate more efficient GA removal algorithms. Full article

Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients’ quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients’ consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame $\mathrm{\Delta }t$ during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient’s consciousness levels. More specifically, increases were observed during this $\mathrm{\Delta }t$, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients’ consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems. Full article

A coarse-graining procedure, which involves averaging time series in non-overlapping windows followed by processing of the obtained multiple data sets, is the initial step in the multiscale entropy computation method. In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. Using the simulated data provided by the interacting nephrons model, we show how this procedure increases, up to 48%, the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations. Based on the experimental data of electrocorticograms (ECoG) of mice, an improvement in differences in local scaling exponents up to 41% and Student’s t-values up to 34% was revealed. Full article

An electrocorticogram (ECoG) is the electrical activity obtainable from the cerebral cortex and an informative source with considerable potential for future advanced applications in various brain-interfacing technologies. Considerable effort has been devoted to developing biocompatible, conformal, soft, and conductive interfacial materials for bridging devices and brain tissue; however, the implementation of brain-adaptive materials with optimized electrical and mechanical characteristics remains challenging. Herein, we present surface electrode arrays using the soft tough ionic conductive hydrogel (STICH). The newly proposed STICH features brain-adaptive softness with Young’s modulus of ~9.46 kPa, which is sufficient to form a conformal interface with the cortex. Additionally, the STICH has high toughness of ~36.85 kJ/mm³, highlighting its robustness for maintaining the solid structure during interfacing with wet brain tissue. The stretchable metal electrodes with a wavy pattern printed on the elastomer were coated with the STICH as an interfacial layer, resulting in an improvement of the impedance from 60 kΩ to 10 kΩ at 1 kHz after coating. Acute in vivo experiments for ECoG monitoring were performed in anesthetized rodents, thereby successfully realizing conformal interfacing to the animal’s cortex and the sensitive recording of electrical activity using the STICH-coated electrodes, which exhibited a higher visual-evoked potential (VEP) amplitude than that of the control device. Full article

Neural interfaces, such as microarrays and probes, consist of many electrodes for stimulating and recording purposes simultaneously. The multi-functional neural interface can suffer from many types of artefacts and noise, such as long-term use, environment, surrounding instruments and living subjects. This paper proposes a filtering approach by enhancing the band-pass and band-stop selection of the Kaiser Window finite-impulse response (FIR) filter based on the occurrence histogram of spectrum bands of neuronal signals in all channels. The implementation of the approach shows a clear enhancement of electrocorticogram (ECoG) signals by keeping the most important features and components, such as the interictal spikes. Full article

Objectives. Ischemic stroke is a leading cause of death and disability worldwide. To search for new therapeutic and pharmacotherapeutic strategies, numerous models of this disease have been proposed, the most popular being transient middle cerebral artery occlusion. Behavioral and sensorimotor testing, biochemical, and histological methods are traditionally used in conjunction with this model to assess the effectiveness of potential treatment options. Despite its wide overall popularity, electroencephalography/electrocorticography is quite rarely used in such studies. Materials and methods. In the present work, we explored the changes in brain electrical activity at days 3 and 7 after 30- and 45-min of transient middle cerebral artery occlusion in rats. Results. Cerebral ischemia altered the amplitude and spectral electrocorticogram characteristics, and led to a reorganization of inter- and intrahemispheric functional connections. Ischemia duration affected the severity as well as the nature of the observed changes. Conclusions. The dynamics of changes in brain electrical activity may indicate a spontaneous partial recovery of impaired cerebral functions at post-surgery day 7. Our results suggest that electrocorticography can be used successfully to assess the functional status of the brain following ischemic stroke in rats as well as to investigate the dynamics of functional recovery. Full article

This article proposes a novel cost-effective method to achieve microfeature-sized patterns on Polydimethylsiloxane (PDMS) substrates. As a biocompatible, flexible, economical, and easy-to-use polymer benefiting the trait of mechanical impedance close to that of soft tissues, PDMS is the best candidate to be used where we need communication between the electrical circuits and soft tissues. Additionally, PDMS can be matched with tissue’s different shapes and doesn’t cause any trauma. The proposed approach eliminates complex and high-cost manufacturing methods of microfeature-sized patterns on PDMS, such as conventional microfabrication methods. Our technique takes advantage of not requiring standard photolithography processes, making it simple and cost-effective. This manner can be used for various purposes, such as micro-fluidic chip fabrication, bio-sensing applications, neuroscience research and neural prosthetics such as electrocorticogram (ECoG) and, in general, where microfeature-size patterning on PDMS is required. To prove the method’s functionality, we fabricated a test sample. Firstly, the scaffold was fabricated using a conventional laser engraver and Poly(methylmethacrylate) (PMMA). Then, a mold was made using this scaffold from PDMS. In the last step, a typical commercial photoresist was applied as an anti-adhesion layer between the PDMS mold and the sample to make the sample peel off the mold surface easily. The final sample indicated that the pattern’s feature size was around 200 micrometers and that the required patterns were very close to the desired form possible. Full article

Implantation of subdural electrodes on the brain surface is still widely performed as one of the “gold standard methods” for the presurgical evaluation of epilepsy. Stereotactic insertion of depth electrodes to the brain can be added to detect brain activities in deep-seated lesions to which surface electrodes are insensitive. This study tried to clarify the efficacy and limitations of combined implantation of subdural and depth electrodes in intractable epilepsy patients. Fifty-three patients with drug-resistant epilepsy underwent combined implantation of subdural and depth electrodes for long-term intracranial electroencephalography (iEEG) before epilepsy surgery. The detectability of early ictal iEEG change (EIIC) were compared between the subdural and depth electrodes. We also examined clinical factors including resection of MRI lesion and EIIC with seizure freedom. Detectability of EIIC showed no significant difference between subdural and depth electrodes. However, the additional depth electrode was useful for detecting EIIC from apparently deep locations, such as the insula and mesial temporal structures, but not in detecting EIIC in patients with ulegyria (glial scar). Total removal of MRI lesion was associated with seizure freedom. Depth electrodes should be carefully used after consideration of the suspected etiology to avoid injudicious usage. Full article

This study describes a method for classifying electrocorticograms (ECoGs) based on motor imagery (MI) on the brain–computer interface (BCI) system. This method is different from the traditional feature extraction and classification method. In this paper, the proposed method employs the deep learning algorithm for extracting features and the traditional algorithm for classification. Specifically, we mainly use the convolution neural network (CNN) to extract the features from the training data and then classify those features by combing with the gradient boosting (GB) algorithm. The comprehensive study with CNN and GB algorithms will profoundly help us to obtain more feature information from brain activities, enabling us to obtain the classification results from human body actions. The performance of the proposed framework has been evaluated on the dataset I of BCI Competition III. Furthermore, the combination of deep learning and traditional algorithms provides some ideas for future research with the BCI systems. Full article

Completely locked-in state (CLIS) patients are unable to speak and have lost all muscle movement. From the external view, the internal brain activity of such patients cannot be easily perceived, but CLIS patients are considered to still be conscious and cognitively active. Detecting the current state of consciousness of CLIS patients is non-trivial, and it is difficult to ascertain whether CLIS patients are conscious or not. Thus, it is important to find alternative ways to re-establish communication with these patients during periods of awareness, and one such alternative is through a brain–computer interface (BCI). In this study, multiscale-based methods (multiscale sample entropy, multiscale permutation entropy and multiscale Poincaré plots) were applied to analyze electrocorticogram signals from a CLIS patient to detect the underlying consciousness level. Results from these different methods converge to a specific period of awareness of the CLIS patient in question, coinciding with the period during which the CLIS patient is recorded to have communicated with an experimenter. The aim of the investigation is to propose a methodology that could be used to create reliable communication with CLIS patients. Full article

Motor coordination and motor learning are well-known roles of the cerebellum. Recent evidence also supports the contribution of the cerebellum to the oscillatory activity of brain networks involved in a wide range of disorders. Kainate, a potent analog of the excitatory neurotransmitter glutamate, can be used to induce dystonia, a neurological movement disorder syndrome consisting of sustained or repetitive involuntary muscle contractions, when applied on the surface of the cerebellum. This research aims to study the interhemispheric cortical communication between the primary motor cortices after repeated kainate application on cerebellar vermis for five consecutive days, in mice. We recorded left and right primary motor cortices electrocorticograms and neck muscle electromyograms, and quantified the motor behavior abnormalities. The results indicated a reduced coherence between left and right motor cortices in low-frequency bands. In addition, we observed a phenomenon of long-lasting adaptation with a modification of the baseline interhemispheric coherence. Our research provides evidence that the cerebellum can control the flow of information along the cerebello-thalamo-cortical neural pathways and can influence interhemispheric communication. This phenomenon could function as a compensatory mechanism for impaired regional networks. Full article

Background: In humans, early pathological activity on invasive electrocorticograms (ECoGs) and its putative association with pathomorphology in the early period of traumatic brain injury (TBI) remains obscure. Methods: We assessed pathological activity on scalp electroencephalograms (EEGs) and ECoGs in patients with acute TBI, early electrophysiological changes after lateral fluid percussion brain injury (FPI), and electrophysiological correlates of hippocampal damage (microgliosis and neuronal loss), a week after TBI in rats. Results: Epileptiform activity on ECoGs was evident in 86% of patients during the acute period of TBI, ECoGs being more sensitive to epileptiform and periodic discharges. A “brush-like” ECoG pattern superimposed over rhythmic delta activity and periodic discharge was described for the first time in acute TBI. In rats, FPI increased high-amplitude spike incidence in the neocortex and, most expressed, in the ipsilateral hippocampus, induced hippocampal microgliosis and neuronal loss, ipsilateral dentate gyrus being most vulnerable, a week after TBI. Epileptiform spike incidence correlated with microglial cell density and neuronal loss in the ipsilateral hippocampus. Conclusion: Epileptiform activity is frequent in the acute period of TBI period and is associated with distant hippocampal damage on a microscopic level. This damage is probably involved in late consequences of TBI. The FPI model is suitable for exploring pathogenetic mechanisms of post-traumatic disorders. Full article

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms. Full article