Neural Activity Research Articles

Astrocytic Regulation of Cocaine Locomotor Sensitization in EcoHIV Infected Mice.

Cocaine use disorder (CUD) is highly comorbid with HIV infection and worsens HIV outcomes. Preclinical research on the outcomes of HIV infection may yield crucial information on neurobehavioral changes resulting from chronic drug exposure in people living with HIV (PLWH). Repeated exposure to cocaine alters behavioral responses to cocaine. This includes development of cocaine locomotor sensitization - or increased locomotor responses to the same doses of cocaine - which depends on nucleus accumbens (NAc) neural plasticity. NAc astrocytes are key regulators of neural activity and plasticity, and their function can be impaired by cocaine exposure and HIV infection, thus implicating them as potential regulators of HIV-induced changes in behavioral response to cocaine. To characterize the effects of HIV infection on cocaine locomotor sensitization, we employed the EcoHIV mouse model to assess changes in locomotor responses after repeated cocaine (10mg/kg) exposure and challenge. EcoHIV infection potentiated expression of cocaine sensitization. We also identified EcoHIV-induced increases in expression of the astrocytic nuclear marker Sox9 selectively in the NAc core. To investigate whether modulation of NAc astrocytes could reverse EcoHIV-induced deficits, we employed a chemogenetic approach. We found that chemogenetic activation of NAc astrocyte Gq signaling attenuated EcoHIV-enhanced cocaine sensitization. We propose that HIV infection contributes to cocaine behavioral sensitization and induces adaptations in NAc astrocytes, while promoting NAc astrocytic Gq-signaling can recover EcoHIV-induced behavioral changes. These findings identify potential cellular substrates of disordered cocaine-driven behavior in the context of HIV infection and point toward strategies to reduce cocaine-related behavior in PLWH.

Study on the impact of aging on the brain activity patterns in auditory speech comprehension dual-route regions

Objective: To elucidate the patterns of neural activity alterations associated with auditory speech comprehension across the lifespan and the impact of varying listening environments on these dynamics. Methods: Functional near-infrared spectroscopy (fNIRS) was employed to measure the concentration of oxygenated hemoglobin in the brains of 93 adults aged from 20 to 70 with normal hearing. These participants were recruited from Beijing Tongren Hospital, affiliated with Capital Medical University, between March 2021 and February 2023. Brain activity was recorded as subjects passively listened to sentences in both silent and noise conditions with varying signal-to-noise ratios (SNR). The alterations in brain activity were analyzed to delineate the age-related trends under different auditory conditions. Statistical analysis was performed using SPSS 22.0 software. Results: The bilateral primary auditory cortex, superior temporal gyrus, and Wernicke's area, critical for sound signal discrimination and perception, exhibited enhanced activity post-stimulus presentation. Broca's area, pivotal for speech production, demonstrated an initial decrease in activity followed by an increment after stimulus onset. The ventral middle temporal gyrus and dorsal postcentral gyrus showed augmented activity in later time windows. Furthermore, it was observed that in quiet conditions and at low noise levels (SNR=10 dB), auditory cortical activity diminished with age. With increasing noise levels (SNR=5 dB), compensatory brain regions (right ventral middle temporal gyrus and dorsal postcentral gyrus) showed enhanced activity with advancing age. As noise intensity further escalated (SNR=0, SNR=-5 dB), not only did auditory cortical activity decline, but also the activity in regions associated with semantic processing and motor functions reduced with age. Conclusion: During auditory speech comprehension, dual-pathway brain regions exhibit distinct activity patterns. With heightened noise exposure, an increasing number of brain regions are influenced by aging, manifesting as a general decline in activity in most dual-pathway regions, alongside a selective augmentation in some compensatory regions on the right hemisphere.

Viral vector eluting lenses for single-step targeted expression of genetically-encoded activity sensors for in vivo microendoscopic calcium imaging.

Optical methods for studying the brain offer powerful approaches for understanding how neural activity underlies complex behavior. These methods typically rely on genetically encoded sensors and actuators to monitor and control neural activity. For microendoscopic calcium imaging, injection of a virus followed by implantation of a lens probe is required to express a calcium sensor and enable optical access to the target brain region. This two-step process poses several challenges, chief among them being the risks associated with mistargeting and/or misalignment between virus expression zone, lens probe and target brain region. Here, we engineer an adeno-associated virus (AAV)-eluting polymer coating for gradient refractive index (GRIN) lenses enabling expression of a genetically encoded calcium indicator (GCaMP) directly within the brain region of interest upon implantation of the lens. This approach requires only one surgical step and guarantees alignment between GCaMP expression and lens in the brain. Additionally, the slow virus release from these coatings increases the working time for surgical implantation, expanding the brain regions and species amenable to this approach. These enhanced capabilities should accelerate neuroscience research utilizing optical methods and advance our understanding of the neural circuit mechanisms underlying brain function and behavior in health and disease.

The function of juvenile-adult transition axis in female sexual receptivity of Drosophila melanogaster.

Female sexual receptivity is essential for reproduction of a species. Neuropeptides play the main role in regulating female receptivity. However, whether neuropeptides regulate female sexual receptivity during the neurodevelopment is unknown. Here, we found the peptide hormone prothoracicotropic hormone (PTTH), which belongs to the insect PG (prothoracic gland) axis, negatively regulated virgin female receptivity through ecdysone during neurodevelopment in Drosophila melanogaster. We identified PTTH neurons as doublesex-positive neurons, they regulated virgin female receptivity before the metamorphosis during the third-instar larval stage. PTTH deletion resulted in the increased EcR-A expression in the whole newly formed prepupae. Furthermore, the ecdysone receptor EcR-A in pC1 neurons positively regulated virgin female receptivity during metamorphosis. The decreased EcR-A in pC1 neurons induced abnormal morphological development of pC1 neurons without changing neural activity. Among all subtypes of pC1 neurons, the function of EcR-A in pC1b neurons was necessary for virgin female copulation rate. These suggested that the changes of synaptic connections between pC1b and other neurons decreased female copulation rate. Moreover, female receptivity significantly decreased when the expression of PTTH receptor Torso was reduced in pC1 neurons. This suggested that PTTH not only regulates female receptivity through ecdysone but also through affecting female receptivity associated neurons directly. The PG axis has similar functional strategy as the hypothalamic-pituitary-gonadal axis in mammals to trigger the juvenile-adult transition. Our work suggests a general mechanism underlying which the neurodevelopment during maturation regulates female sexual receptivity.

Decoding working-memory load during n-back task performance from high channel fNIRS data

Objective.Functional near-infrared spectroscopy (fNIRS) can measure neural activity through blood oxygenation changes in the brain in a wearable form factor, enabling unique applications for research in and outside the lab and in practical occupational settings. fNIRS has proven capable of measuring cognitive states such as mental workload, often using machine learning (ML) based brain-computer interfaces (BCIs). To date, this research has largely relied on probes with channel counts from under ten to several hundred, although recently a new class of wearable NIRS devices featuring thousands of channels has emerged. This poses unique challenges for ML classification, as fNIRS is typically limited by few training trials which results in severely under-determined estimation problems. So far, it is not well understood how such high-resolution data is best leveraged in practical BCIs and whether state-of-the-art or better performance can be achieved.Approach.To address these questions, we propose an ML strategy to classify working-memory load that relies on spatio-temporal regularization and transfer learning from other subjects in a combination that, to our knowledge, has not been used in previous fNIRS BCIs. The approach can be interpreted as an end-to-end generalized linear model and allows for a high degree of interpretability using channel-level or cortical imaging approaches.Main results.We show that using the proposed methodology, it is possible to achieve state-of-the-art decoding performance with high-resolution fNIRS data. We also replicated several state-of-the-art approaches on our dataset of 43 participants wearing a 3198 dual-channel NIRS device while performing then-Back task and show that these existing methodologies struggle in the high-channel regime and are largely outperformed by the proposed pipeline.Significance.Our approach helps establish high-channel NIRS devices as a viable platform for state-of-the-art BCI and opens new applications using this class of headset while also enabling high-resolution model imaging and interpretation.

Dissociative and prioritized modeling of behaviorally relevant neural dynamics using recurrent neural networks

Understanding the dynamical transformation of neural activity to behavior requires new capabilities to nonlinearly model, dissociate and prioritize behaviorally relevant neural dynamics and test hypotheses about the origin of nonlinearity. We present dissociative prioritized analysis of dynamics (DPAD), a nonlinear dynamical modeling approach that enables these capabilities with a multisection neural network architecture and training approach. Analyzing cortical spiking and local field potential activity across four movement tasks, we demonstrate five use-cases. DPAD enabled more accurate neural–behavioral prediction. It identified nonlinear dynamical transformations of local field potentials that were more behavior predictive than traditional power features. Further, DPAD achieved behavior-predictive nonlinear neural dimensionality reduction. It enabled hypothesis testing regarding nonlinearities in neural–behavioral transformation, revealing that, in our datasets, nonlinearities could largely be isolated to the mapping from latent cortical dynamics to behavior. Finally, DPAD extended across continuous, intermittently sampled and categorical behaviors. DPAD provides a powerful tool for nonlinear dynamical modeling and investigation of neural–behavioral data.

The oxytocinergic system and racial ingroup bias in empathic neural activity

Studies have indicated that the human brain exhibits a more robust neural empathic response towards individuals of the same racial ingroup than those of the outgroup. However, the impact of the oxytocinergic system on the dynamic connectivity between brain regions involved in racial ingroup bias in empathy (RIBE) and its implications for real-life social interaction intention remains unclear. To address this gap, we employed functional magnetic resonance imaging (fMRI) to investigate RIBE-modulated neural activities and the influence of the oxytocinergic system at both neural and behavioral levels. Participants homozygous for the A/A and G/G genotypes of the oxytocin receptor gene (OXTR) rs53576 polymorphism underwent scanning while making judgments about painful versus non-painful stimuli in same-race versus other-race scenarios following either oxytocin (OT) or placebo treatment. The results revealed greater activity in the anterior cingulate cortex (ACC) and anterior insula (AI) in response to same-race compared to other-race models in the G/G group but not in the A/A group. RIBE also modulated the connections between bilateral AI and the ACC, and the effect of OT on this modulatory effect was moderated by genotype rs53576 and interpersonal trust. Moreover, more extensive changes in AI-ACC connections were associated with higher levels of revenge intention in the low interpersonal trust group. Overall, our findings suggest a pivotal role of the oxytocinergic system in the RIBE-modulated neural activities and revenge intention in human interactions with the modulatory effect of interpersonal trust.This article is part of the Special Issue on “Empathic Pain”.

A flexible high-precision photoacoustic retinal prosthesis.

Retinal degenerative diseases of photoreceptors are a leading cause of blindness with no effective treatment. Retinal prostheses seek to restore sight by stimulating remaining retinal cells. We here present a photoacoustic retinal stimulation technology. We designed a polydimethylsiloxane and carbon-based flexible film that converts near-infrared laser pulses into a localized acoustic field, aiming at high-precision acoustic activation of mechanosensitive retinal cells. This photoacoustic stimulation of wild-type and degenerated ex vivo retinae resulted in robust and localized retinal ganglion cell activation with sub-100-µm resolution in both wild-type and degenerated ex vivo retinae. Our millimeter-size photoacoustic film generated neural activation in vivo along the visual pathway to the superior colliculus, as measured by functional ultrasound imaging when the film was implanted in the rat subretinal space and stimulated by pulsed laser. Biosafety of the film was indicated by absence of short-term adverse effect under optical coherence tomography retinal imaging, while local thermal increase was measured below 1 °C. These findings demonstrate the potential of our photoacoustic stimulation for visual restoration in blind patients with a high spatial precision and a large field of view.

Effective Connectivity Network of Aberrant Prediction Error Processing in Auditory Phantom Perception.

Introduction: Prediction error (PE) is key to perception in the predictive coding framework. However, previous studies indicated the varied neural activities evoked by PE in tinnitus patients. Here, we aimed to reconcile the conflict by (1) a more nuanced view of PE, which could be driven by changing stimulus (stimulus-driven PE [sPE]) and violation of current context (context-driven PE [cPE]) and (2) investigating the aberrant connectivity networks that are engaged in the processing of the two types of PEs in tinnitus patients. Methods: Ten tinnitus patients with normal hearing and healthy controls were recruited, and a local-global auditory oddball paradigm was applied to measure the electroencephalographic difference between the two groups during sPE and cPE conditions. Results: Overall, the sPE condition engaged bottom-up and top-down connections, whereas the cPE condition engaged mostly top-down connections. The tinnitus group showed decreased sensitivity to the sPE and increased sensitivity to the cPE condition. Particularly, the auditory cortex and posterior cingulate cortex were the hubs for processing cPE in the control and tinnitus groups, respectively, showing the orientation to an internal state in tinnitus. Furthermore, tinnitus patients showed stronger connectivity to the parahippocampus and pregenual anterior cingulate cortex for the establishment of the prediction during the cPE condition. Conclusion: These results begin to dissect the role of changes in stimulus characteristics versus changes in the context of processing the same stimulus in mechanisms of tinnitus generation.

Disturbed Dynamic Brain Activity and Neurovascular Coupling in End-Stage Renal Disease Assessed With MRI.

Pathophysiological mechanisms underlying cognitive impairment in end-stage renal disease (ESRD) remain unclear, with limited studies on the temporal variability of neural activity and its coupling with regional perfusion. To assess neural activity and neurovascular coupling (NVC) in ESRD patients, evaluate the classification performance of these abnormalities, and explore their relationships with cognitive function. Prospective. Exactly 33 ESRD patients and 35 age, sex, and education matched healthy controls (HCs). The 3.0T/3D pseudo-continuous arterial spin labeling, resting-state functional MRI, and 3D-T1 weighted structural imaging. Dynamic (dfALFF) and static (sfALFF) fractional amplitude of low-frequency fluctuations and cerebral blood flow (CBF) were assessed. CBF-fALFF correlation coefficients and CBF/fALFF ratio were determined for ESRD patients and HCs. Their ability to distinguish ESRD patients from HCs was evaluated, alongside assessment of cerebral small vessel disease (CSVD) MRI features. All participants underwent blood biochemical and neuropsychological tests to evaluate cognitive decline. Chi-squared test, two-sample t-test, Mann-Whitney U tests, covariance analysis, partial correlation analysis, family-wise error, false discovery rate, Bonferroni correction, area under the receiver operating characteristic curve (AUC) and multivariate pattern analysis. P < 0.05 denoted statistical significance. ESRD patients exhibited higher dfALFF in triangular part of left inferior frontal gyrus (IFGtriang) and left middle temporal gyrus, lower CBF/dfALFF ratio in multiple brain regions, and decreased CBF/sfALFF ratio in bilateral superior temporal gyrus (STG). Compared with CBF/sfALFF ratio, dfALFF, and sfALFF, CBF/dfALFF ratio (AUC = 0.916) achieved the most powerful classification performance in distinguishing ESRD patients from HCs. In ESRD patients, decreased CBF/fALFF ratio correlated with more severe renal impairment, increased CSVD burden, and cognitive decline (0.4 < |r| < 0.6). ESRD patients exhibited abnormal dynamic brain activity and impaired NVC, with dynamic features demonstrating superior discriminative capacity and CBF/dfALFF ratio showing powerful classification performance. 1 TECHNICAL EFFICACY: Stage 1.

Task-based functional neural correlates of social cognition across autism and schizophrenia spectrum disorders

BackgroundAutism and schizophrenia spectrum disorders (SSDs) both feature atypical social cognition. Despite evidence for comparable group-level performance in lower-level emotion processing and higher-level mentalizing, limited research has examined the neural basis of social cognition across these conditions. Our goal was to compare the neural correlates of social cognition in autism, SSDs, and typically developing controls (TDCs).MethodsData came from two harmonized studies in individuals diagnosed with autism or SSDs and TDCs (aged 16–35 years), including behavioral social cognitive metrics and two functional magnetic resonance imaging (fMRI) tasks: a social mirroring Imitate/Observe (ImObs) task and the Empathic Accuracy (EA) task. Group-level comparisons, and transdiagnostic analyses incorporating social cognitive performance, were run using FSL’s PALM for each task, covarying for age and sex (1000 permutations, thresholded at p < 0.05 FWE-corrected). Exploratory region of interest (ROI)-based analyses were also conducted.ResultsImObs and EA analyses included 164 and 174 participants, respectively (autism N = 56/59, SSD N = 50/56, TDC N = 58/59). EA and both lower- and higher-level social cognition scores differed across groups. While canonical social cognitive networks were activated, no significant whole-brain or ROI-based group-level differences in neural correlates for either task were detected. Transdiagnostically, neural activity during the EA task, but not the ImObs task, was associated with lower- and higher-level social cognitive performance.LimitationsDespite attempting to match our groups on age, sex, and race, significant group differences remained. Power to detect regional brain differences is also influenced by sample size and multiple comparisons in whole-brain analyses. Our findings may not generalize to autism and SSD individuals with co-occurring intellectual disabilities.ConclusionsThe lack of whole-brain and ROI-based group-level differences identified and the dimensional EA brain-behavior relationship observed across our sample suggest that the EA task may be well-suited to target engagement in novel intervention testing. Our results also emphasize the potential utility of cross-condition approaches to better understand social cognition across autism and SSDs.

CONTROL OF DENTINE HYPERSENSITIVITY WITH THE USE OF DESENSITIZING: Case report of a patient from the city of Mato Verde - MG

Dentin hypersensitivity is characterized by an exacerbated response to thermal, chemical, osmotic and tactile sensory stimuli in the area where the dentin is exposed, causing acute and short-lasting painful symptoms. This study aimed to report a clinical case of control of dentin hypersensitivity using desensitizers. A qualitative study with observational/descriptive objectives, carried out in the municipality of Mato Verde-MG at Faculdade Verde Norte (FAVENORTE).The study was approved by the Research Ethics Committee, with opinion no. 6,985,721. A 22-year-old female patient attended dental care at the FAVENORTE Dental Clinic, complaining of intense sensitivity in several teeth. A medical and dental history was collected during anamnesis, as well as information regarding routine and lifestyle. On clinical examination, gingival recession and root exposure were found in elements 14, 15, 16, 24, 25, 34, 35, 44, 45, without loss of tooth structure. In the occlusal assessment, no changes were observed. The patient reports brushing teeth more than four times a day and using excessive force when brushing. After evaluation, it was concluded that the main etiological factor of the injuries was caused by a mechanical factor. For treatment, a desensitizer based on potassium nitrate (neural action) with sodium fluoride (obliterating action), Desensibilize KF 2%, was used. In the treatment of dentin hypersensitivity in the case described, the use of desensitizer, oral hygiene instructions, associated with maintenance with the use of dentifrice, proved to be a satisfactory procedure, since at the end of the treatment, the degree of discomfort related to the hypersensitivity condition dentin previously reported by the patient, ceased to exist. Therefore, the use of desensitizing agents is a low-cost procedure with excellent results.

Effects of observing own/others hand movement in different perspectives on mu rhythm suppression: an EEG study

BackgroundPrevious studies have reported that the sense of “self” is associated with specific brain regions and neural network activities. In addition, the mirror system, which functions when executing or observing an action, might contribute to differentiating the self from others and form the basis of the sense of self as a fundamental physical representation. This study investigated whether differences in mu suppression, an indicator of mirror system activity, reflect cognitions related to self-other discrimination.MethodsThe participants were 30 of healthy college students. The participants observed short video clips of hand movements performed by themselves or actors from two perspectives (i.e., first-person and third-person). The electroencephalogram (EEG) mu rhythm (8–13 Hz) was measured during video observation as an index of mirror neuron system activity. EEG activity related to self-detection was analyzed using participants’ hand movements as self-relevant stimuli.ResultsThe results showed that mu suppression in the 8–13-Hz range exhibited perspective-dependent responses to self/other stimuli. There was a significant self-oriented mu suppression response in the first-person perspective. However, the study found no significant response orientation in the third-person perspective. The results suggest that mirror system activity may involve self-other discrimination differently depending on the perspective.ConclusionsIn summary, this study examined the mirror system’s activity for self and others using the EEG’s mu suppression. As a result, it was suggested that differences in self and others or perspectives may influence mu suppression.

Robustly encoding certainty in a metastable neural circuit model.

Localized persistent neural activity can encode delayed estimates of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons along their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equationsfor the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equationsaccurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with experiments showing certainty correlates with more accurate memories.

Learning to integrate parts for whole through correlated neural variability.

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing.

Human Pluripotent Stem Cell-Derived Astrocyte Functionality Compares Favorably with Primary Rat Astrocytes.

Astrocytes are essential for the formation and maintenance of neural networks. However, a major technical challenge for investigating astrocyte function and disease-related pathophysiology has been the limited ability to obtain functional human astrocytes. Despite recent advances in human pluripotent stem cell (hPSC) techniques, primary rodent astrocytes remain the gold standard in coculture with human neurons. We demonstrate that a combination of leukemia inhibitory factor (LIF) and bone morphogenetic protein-4 (BMP4) directs hPSC-derived neural precursor cells to a highly pure population of astroglia in 28 d. Using single-cell RNA sequencing, we confirm the astroglial identity of these cells and highlight profound transcriptional adaptations in cocultured hPSC-derived astrocytes and neurons, consistent with their further maturation. In coculture with human neurons, multielectrode array recordings revealed robust network activity of human neurons in a coculture with hPSC-derived or rat astrocytes [3.63 ± 0.44 min-1 (hPSC-derived), 2.86 ± 0.64 min-1 (rat); p = 0.19]. In comparison, we found increased spike frequency within network bursts of human neurons cocultured with hPSC-derived astrocytes [56.31 ± 8.56 Hz (hPSC-derived), 24.77 ± 4.04 Hz (rat); p < 0.01], and whole-cell patch-clamp recordings revealed an increase of postsynaptic currents [2.76 ± 0.39 Hz (hPSC-derived), 1.07 ± 0.14 Hz (rat); p < 0.001], consistent with a corresponding increase in synapse density [14.90 ± 1.27/100 μm2 (hPSC-derived), 8.39 ± 0.63/100 μm2 (rat); p < 0.001]. Taken together, we show that hPSC-derived astrocytes compare favorably with rat astrocytes in supporting human neural network activity and maturation, providing a fully human platform for investigating astrocyte function and neuronal-glial interactions.

Brain Neuroplasticity Leveraging Virtual Reality and Brain-Computer Interface Technologies.

This study explores neuroplasticity through the use of virtual reality (VR) and brain-computer interfaces (BCIs). Neuroplasticity is the brain's ability to reorganize itself by forming new neural connections in response to learning, experience, and injury. VR offers a controlled environment to manipulate sensory inputs, while BCIs facilitate real-time monitoring and modulation of neural activity. By combining VR and BCI, researchers can stimulate specific brain regions, trigger neurochemical changes, and influence cognitive functions such as memory, perception, and motor skills. Key findings indicate that VR and BCI interventions are promising for rehabilitation therapies, treatment of phobias and anxiety disorders, and cognitive enhancement. Personalized VR experiences, adapted based on BCI feedback, enhance the efficacy of these interventions. This study underscores the potential for integrating VR and BCI technologies to understand and harness neuroplasticity for cognitive and therapeutic applications. The researchers utilized the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method to conduct a comprehensive and systematic review of the existing literature on neuroplasticity, VR, and BCI. This involved identifying relevant studies through database searches, screening for eligibility, and assessing the quality of the included studies. Data extraction focused on the effects of VR and BCI on neuroplasticity and cognitive functions. The PRISMA method ensured a rigorous and transparent approach to synthesizing evidence, allowing the researchers to draw robust conclusions about the potential of VR and BCI technologies in promoting neuroplasticity and cognitive enhancement.

Maintenance of delay-period activity in working memory task is modulated by local network structure.

Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.

Cerebellar activity in hemi-parkinsonian rats during volitional gait and freezing.

Parkinson's disease is a neurodegenerative disease characterized by gait dysfunction in the advanced stages of the disease. The unilateral 6-hydroxydopamine toxin-induced model is the most studied animal model of Parkinson's disease, which reproduces gait dysfunction after >68% dopamine loss in the substantia nigra pars compacta. The extent to which the neural activity in hemi-parkinsonian rats correlates to gait dysfunction and dopaminergic cell loss is not clear. In this article, we report the effects of unilateral dopamine depletion on cerebellar vermis activity using micro-electrocorticography during walking and freezing on a runway. Gait and neural activity were measured in 6-hydroxydopamine- and sham-lesioned rats aged between 4 and 5 months at 14, 21 and 28 days after infusion of 6-hydroxydopamine or control vehicle into the medial forebrain bundle (n = 20). Gait deficits in 6-hydroxydopamine rats were different from sham rats at 14 days (P < 0.05). Gait deficits in 6-hydroxydopamine rats improved at 21 and 28 days except for run speed, which decreased at 28 days (P = 0.018). No differences in gait deficits were observed in sham-lesioned rats at any time points. Hemi-parkinsonian rats showed hyperactivity in the cerebellar vermis at 21 days (P < 0.05), but not at 14 and 28 days, and the activity was reduced during freezing epochs in Lobules VIa, VIb and VIc (P < 0.05). These results suggest that dopaminergic cell loss causes pathological cerebellar activity at 21 days post-lesion and suggest that compensatory mechanisms from the intact hemisphere contribute to normalized cerebellar activity at 28 days. The decrease in cerebellar oscillatory activity during freezing may be indicative of neurological changes during freezing of gait in patients with Parkinson's disease making this region a potential location for biomarker detection. Although the unilateral 6-hydroxydopamine model presents gait deficits that parallel clinical presentations of Parkinson's disease, further studies in animal models of bilateral dopamine loss are needed to understand the role of the cerebellar vermis in Parkinson's disease.

Patterns identification using blind source separation with application to neural activities associated with anticipated falls

Exploring the nature of neural activity in humans attracts much attention in human-machine interfaces. However, there is still a lack of understanding about the neural activities associated with human anticipated falls. This study examines how the brain responds to anticipated falls by identifying the patterns of neural activities associated with anticipated falls with Electroencephalograms (EEG). We developed a method for pattern identification of neural activities with second-order blind identification in the machine learning framework. The effects of artifact removal within EEG data are compared between the second-order blind identification (SOBI) algorithm and a related method. Experiments involving 135 anticipated falls and 135 walk controls show that the median sample entropy of the EEG signals in the anticipated falls is from 0.1151 to 0.2267 smaller (p-value <0.01) than those in the walk controls. In anticipated falls detection, the average accuracy with random forests is 85.2% for channel O2, which is nearly 10% higher than the channel temporal cerebral cortex (T3 and T4). The results can further guide us in developing new systems of human–machine interfaces, such as EEG data-augmented wearable airbags, to prevent anticipated falls.