Neuronal Oscillations Research Articles

Modeling of the metal–insulator transition temperature in alio-valently doped VO2 through symbolic regression

The correlated semiconductor vanadium dioxide (VO2) exhibits an insulator–metal transition (IMT) near room temperature, which is of interest in various device applications. Precise IMT temperature control is crucial to determine the use cases across technologies such as thermochromic windows, actuators for robots or neuronal oscillators. Doping the cation or anion sites can modulate the IMT by several tens of degrees and control hysteresis. However, modeling the effects of control parameters (e.g., doping concentration, type of dopants) is challenging due to complex experimental procedures and limited data, hindering the use of traditional data-driven machine learning approaches. Symbolic regression (SR) can bridge this gap by identifying nonlinear expressions connecting key input parameters to target properties, even with small data sets. In this work, we develop SR models to capture the IMT trends in VO2 influenced by different dopant parameters. Using experimental data from the literature, our study reveals a dual nature of the IMT temperature with varying tungsten (W) doping concentrations. The symbolic model captures data trends and accounts for experimental variability, providing a complementary approach to first-principles calculations. Our feature-driven analysis across a broader class of dopants informs selectivity and provides qualitative insights into tuning phase transition properties valuable for neuromorphic computing and thermochromic windows.

Theta-gamma-coupling as predictor of working memory performance in young and elderly healthy people

The relationship between working memory (WM) and neuronal oscillations can be studied in detail using brain stimulation techniques, which provide a method for modulating these oscillations and thus influencing WM. The endogenous coupling between the amplitude of gamma oscillations and the phase of theta oscillations is crucial for cognitive control. Theta/gamma peak-coupled transcranial alternating current stimulation (TGCp-tACS) can modulate this coupling and thus influence WM performance. This study investigated the effects of TGCp-tACS on WM in older adults and compared their responses with those of younger participants from our previous work who underwent the same experimental design. Twenty-eight older subjects underwent both TGCp-tACS and sham stimulation sessions at least 72 h apart. Resting-state electroencephalography (EEG) was recorded before and after the interventions, and a WM task battery with five different WM tasks was performed during the interventions to assess various WM components. Outcomes measured included WM task performance (e.g., accuracy, reaction time (RT)) and changes in power spectral density (PSD) in different frequency bands. TGCp-tACS significantly decreased accuracy and RT on the 10- and 14-point Sternberg tasks and increased RT on the Digit Symbol Substitution Test in older adults. In contrast, younger participants showed a significant increase in accuracy only on the 14-item Sternberg task. Electrophysiological analysis revealed a decrease in delta and theta PSD and an increase in high gamma PSD in both younger and older participants after verum stimulation. In conclusion, theta-gamma coupling is essential for WM and modulation of this coupling affects WM performance. The effects of TGCp-tACS on WM vary with age due to natural brain changes. To better support older adults, the study suggests several strategies to improve cognitive function, including: Adjusting stimulation parameters, applying stimulation to two sites, conducting multiple sessions, and using brain imaging techniques for precise targeting.

7170 Plasticity of the Hypothalamic Tuberoinfundibular Dopaminergic Neuronal Network in Lactating Rats

Abstract Disclosure: P. Papaioannou: None. T. Georgescu: None. D.R. Grattan: None. S. Bunn: None. S. Yip: None. The tuberoinfundibular dopaminergic (TIDA) neurons play a crucial role in regulating prolactin secretion. Their synchronized network activity with slow but highly rhythmic firing pattern is important for dopamine release in rats[1]. While this unique TIDA network activity is vital for prolactin negative feedback in non-lactating conditions, its behaviour during lactation, when prolactin demand is high, remains unknown. Our hypothesis posited that the TIDA neuronal network in lactating rats becomes desynchronized, disrupting the negative feedback loop. To test this, we utilized ex-vivo Ca2+ imaging to simultaneously monitor population-wide TIDA neuron activity in non-lactating (NL; n=13) and lactating (L; n=10) rats injected with a cre-inducible adeno-associated virus (AAV) containing the Ca2+ indicator, GCaMP6s, into their arcuate nucleus. Analysis of neuronal network synchronicity revealed significant desynchronization in the TIDA network during lactation, as indicated by a markedly lower mean correlation coefficient matrix (CM) compared to non-lactating conditions (NL: 0.87±0.02, n= 26 sections vs L: 0.22±0.03, n=29 sections; p<0.001, Student’s t-test). Furthermore, the oscillatory activity patterns of these desynchronized TIDA neurons displayed a significantly lower cell rhythmicity index (RI) in lactating animals compared to non-lactating ones (NL: 0.17±0.01, n=186 cells vs L: 0.70±0.02, n=77 cells; p<0.001, Student’s t-test). Interestingly, within these low rhythmic neurons, some exhibited higher while others showed lower firing frequencies compared to non-lactating TIDA neurons. After 7 days post-weaning (n=4 animals), the TIDA network activity returned to non-lactating behaviour (CM = 0.85 ± 0.04, n=6 sections and RI = 0.17±0.01; n=47 cells). In summary, our findings demonstrate a reversible reconfiguration of the TIDA neuronal networks during lactation, characterized by low rhythmic oscillations and individual unique firing patterns, leading to diminished intercellular synchrony that may impede dopamine release, ultimately facilitating prolactin release to support lactation. Once weaned, the TIDA neuronal network reset to NL state to allow a new reproductive cycle. [1] Lyons, D. J., Horjales-Araujo, E. & Broberger, C. Synchronized network oscillations in rat tuberoinfundibular dopamine neurons: switch to tonic discharge by thyrotropin-releasing hormone. Neuron 65, 217-229, doi:10.1016/j.neuron.2009.12.024 (2010). Presentation: 6/2/2024

Infrared neuroglial modulation of spinal locomotor networks

Infrared neural stimulation (INS) emerges as a promising tool for stimulating the nervous system by its high spatial precision and absence of the use of exogenous agents into the tissue, which led to the first successful proof of concept in human brain. While neural networks have been the focal point of INS research, this technique is also non cell type specific as it triggers activity in non electrically excitable cells. Despite increasing interest, there remains to demonstrate well defined simultaneous astrocytic and neuronal signals in response to INS. Using calcium imaging, we show that INS has the capacity to initiate calcium signaling in both astrocytes and neurons simultaneously from the rostral lumbar spinal cord, each exhibiting distinct temporal and amplitude characteristics. Importantly, the mechanism underlying infrared-induced neuronal and astrocytic calcium signaling differ, with neuronal activity relying on sodium channels, whereas induced astrocytic signaling is predominantly influenced by extracellular calcium and TRPV4 channels. Furthermore, our findings demonstrate the frequency shift of neuronal calcium oscillations through infrared stimulation. By deepening our understanding in INS fundamentals, this technique holds great promise for advancing neuroscience, deepening our understanding of pathologies, and potentially paving the way for future clinical applications.

Multi-timescale compound oscillations in pyramidal neurons: insights from a three-compartment model

Multi-timescale compound oscillations in pyramidal neurons: insights from a three-compartment model

Activation of D2-like dopamine receptors improves the neuronal network and cognitive function of PPT1KI mice.

Palmitoyl-protein thioesterase 1 (PPT1) is a lysosomal depalmitoylation enzyme that mediates protein posttranslational modifications. Loss-of-function mutation of PPT1 causes a failure of the lysosomal degradation of palmitoylated proteins and results in a congenital disease characterized by progressive neuronal degeneration referred to as infantile neuronal ceroid lipofuscinosis (INCL). A mouse knock-in model of PPT1 (PPT1-KI) was established by introducing the R151X mutation into exon 5 of the PPT1 gene, which exhibited INCL-like pathological lesions. We previously reported that hippocampal γ oscillations were impaired in PPT1 mice. Hippocampal γ oscillations can be enhanced by selective activation of the dopamine D4 receptor (DR4), a dopamine D2-like receptor. In this study, we investigated the changes in DR expression and the effects of dopamine and various DR agonists on neural network activity, cognition and motor function in PPT1KI mice. Cognition and motor defects were evaluated via Y-maze, novel object recognition and rotarod tests. Extracellular field potentials were elicited in hippocampal slices, and neuronal network oscillations in the gamma frequency band (γ oscillations) were induced by perfusion with kainic acid (200 nM). PPT1KI mice displayed progressive impairments in γ oscillations and hippocampus-related memory, as well as abnormal expression profiles of dopamine receptors with preserved expression of DR1 and 3, increased membrane expression of DR4 and decreased DR2 levels. The immunocytochemistry analysis revealed the colocalization of PPT1 with DR4 or DR2 in the soma and large dendrites of both WT and PPT1KI mice. Immunoprecipitation confirmed the interaction between PPT1 and DR4 or DR2. The impaired γ oscillations and cognitive functions were largely restored by the application of exogenous dopamine, the selective DR2 agonist quinpirole or the DR4 agonist A412997. Furthermore, the administration of A412997 (0.5 mg/kg, i.p.) significantly upregulated the activity of CaMKII in the hippocampus of 5-month-old PPT1KI mice. Collectively, these results suggest that the activation of D2-like dopamine receptors improves cognition and network activity in PPT1KI mice and that specific DR subunits may be potential targets for the intervention of neurodegenerative disorders, such as INCL.

Oscillations in an artificial neural network convert competing inputs into a temporal code.

The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and complex cells in early visual cortex. However, the computational relevance of oscillatory dynamics experimentally observed in the visual system are typically not considered in artificial neural networks (ANNs). Computational models of neocortical dynamics, on the other hand, rarely take inspiration from computer vision. Here, we combine methods from computational neuroscience and machine learning to implement multiplexing in a simple ANN using oscillatory dynamics. We first trained the network to classify individually presented letters. Post-training, we added temporal dynamics to the hidden layer, introducing refraction in the hidden units as well as pulsed inhibition mimicking neuronal alpha oscillations. Without these dynamics, the trained network correctly classified individual letters but produced a mixed output when presented with two letters simultaneously, indicating a bottleneck problem. When introducing refraction and oscillatory inhibition, the output nodes corresponding to the two stimuli activate sequentially, ordered along the phase of the inhibitory oscillations. Our model implements the idea that inhibitory oscillations segregate competing inputs in time. The results of our simulations pave the way for applications in deeper network architectures and more complicated machine learning problems.

Photoinduced Negative Differential Resistance at a Graphene/Silicon Interface: A Nonadiabatic Quantum Molecular Dynamics Study.

The oscillatory retinal neuron (ORN) is a promising technology for achieving in-sensor cognitive image computing without external power. While its operation is based on photoinduced negative differential resistance (NDR) at a graphene/silicon interface to directly convert the incident optical signal into voltage oscillations, the optoelectronic mechanism of the NDR remains elusive. Here, nonadiabatic quantum molecular dynamics simulations show that the interplay of band alignment and charge transfer rates of photoexcited carriers at varying applied voltages gives rise to NDR at a graphene/silicon interface under illumination. Such intrinsic NDR at an interface, along with extrinsic circuit-level factors, could enable the much needed rational design of desired image computing functionality of ORN devices in the era of ubiquitous AI on edge devices.

Melatonin Regulates Neuronal Synaptic Plasticity in the Supramammillary Nucleus and Attenuates Methamphetamine-Induced Conditioned Place Preference and Sensitization in Mice.

Methamphetamine (METH) is an addictive drug that threatens human health. The supramammillary nucleus (SuM) and its neural circuits play key roles in the regulation of spatial memory retrieval, and hippocampal contextual or social memory. Melatonin (MLT), a pineal hormone, can regulate hypothalamic-neurohypophysial activity. Our previous study showed that MLT attenuates METH-induced locomotor sensitization. However, whether MLT regulates SuM function and participates in METH-induced contextual memory retrieval remains unclear. Using a mouse model of METH-conditioned place preference (CPP) and sensitization, we found that METH activated c-Fos expression and elevated calcium (Ca²⁺) levels in SuM neurons. Chemogenetic inhibition of SuM attenuates CPP and sensitization. Pretreatment with MLT decreased c-Fos expression and Ca2+ levels in the SuM and reversed METH-induced addictive behavior, effects that were blocked with the selective MT2 receptors antagonist 4P-PDOT and the MT1 receptors antagonist S26131. Furthermore, MLT reduced SuM synaptic plasticity, glutamate (Glu) release, and neuronal oscillations caused by METH, which were blocked by 4P-PDOT. In conclusion, our data revealed that MLT regulates neuronal synaptic plasticity in the SuM, likely through the MLT receptors (MTs), and plays a role in modulating METH-addictive behavior.

Multiple Intrinsic Timescales Govern Distinct Brain States in Human Sleep.

Human sleep exhibits multiple, recurrent temporal regularities, ranging from circadian rhythms to sleep stage cycles and neuronal oscillations during nonrapid eye movement sleep. Moreover, recent evidence revealed a functional role of aperiodic activity, which reliably discriminates different sleep stages. Aperiodic activity is commonly defined as the spectral slope χ of the 1/frequency (1/fχ) decay function of the electrophysiological power spectrum. However, several lines of inquiry now indicate that the aperiodic component of the power spectrum might be better characterized by a superposition of several decay processes with associated timescales. Here, we determined multiple timescales, which jointly shape aperiodic activity using human intracranial electroencephalography. Across three independent studies (47 participants, 23 female), our results reveal that aperiodic activity reliably dissociated sleep stage-dependent dynamics in a regionally specific manner. A principled approach to parametrize aperiodic activity delineated several, spatially and state-specific timescales. Lastly, we employed pharmacological modulation by means of propofol anesthesia to disentangle state-invariant timescales that may reflect physical properties of the underlying neural population from state-specific timescales that likely constitute functional interactions. Collectively, these results establish the presence of multiple intrinsic timescales that define the electrophysiological power spectrum during distinct brain states.

High-Frequency Imaging Reveals Synchronised Delta- and Theta-Band Ca2+ Oscillations in the Astrocytic Soma In Vivo.

One of the major breakthroughs of neurobiology was the identification of distinct ranges of oscillatory activity in the neuronal network that were found to be responsible for specific biological functions, both physiological and pathological in nature. Astrocytes, physically coupled by gap junctions and possessing the ability to simultaneously modulate the functions of a large number of surrounding synapses, are perfectly positioned to introduce synchronised oscillatory activity into the neural network. However, astrocytic somatic calcium signalling has not been investigated to date in the frequency ranges of common neuronal oscillations, since astrocytes are generally considered to be slow responders in terms of Ca2+ signalling. Using high-frequency two-photon imaging, we reveal fast Ca2+ oscillations in the soma of astrocytes in the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands in vivo in the rat cortex under ketamine-xylazine anaesthesia, which is known to induce permanent slow-wave sleep. The high-frequency astrocytic Ca2+ signals were not observed under fentanyl anaesthesia, excluding the possibility that the signals were introduced by motion artefacts. We also demonstrate that these fast astrocytic Ca2+ signals, previously considered to be exclusive to neurons, are present in a large number of astrocytes and are phase synchronised at the astrocytic network level. We foresee that the disclosure of these high-frequency astrocytic signals may help with understanding the appearance of synchronised oscillatory signals and may open up new avenues of treatment for neurological conditions characterised by altered neuronal oscillations.

Ultralow Power In-Sensor Neuronal Computing with Oscillatory Retinal Neurons for Frequency-Multiplexed, Parallel Machine Vision.

In-sensor and near-sensor computing architectures enable multiply accumulate operations to be carried out directly at the point of sensing. In-sensor architectures offer dramatic power and speed improvements over traditional von Neumann architectures by eliminating multiple analog-to-digital conversions, data storage, and data movement operations. Current in-sensor processing approaches rely on tunable sensors or additional weighting elements to perform linear functions such as multiply accumulate operations as the sensor acquires data. This work implements in-sensor computing with an oscillatory retinal neuron device that converts incident optical signals into voltage oscillations. A computing scheme is introduced based on the frequency shift of coupled oscillators that enables parallel, frequency multiplexed, nonlinear operations on the inputs. An experimentally implemented 3 × 3 focal plane array of coupled neurons shows that functions approximating edge detection, thresholding, and segmentation occur in parallel. An example of inference on handwritten digits from the MNIST database is also experimentally demonstrated with a 3 × 3 array of coupled neurons feeding into a single hidden layer neural network, approximating a liquid-state machine. Finally, the equivalent energy consumption to carry out image processing operations, including peripherals such as the Fourier transform circuits, is projected to be <20 fJ/OP, possibly reaching as low as 15 aJ/OP.

Computational modeling of the synergistic role of GCN2 and the HPA axis in regulating the integrated stress response in the central circadian timing system.

The circadian timing system and integrated stress response (ISR) systems are fundamental regulatory mechanisms that maintain body homeostasis. The central circadian pacemaker in the suprachiasmatic nucleus (SCN) governs daily rhythms through interactions with peripheral oscillators via the hypothalamus-pituitary-adrenal (HPA) axis. On the other hand, ISR signaling is pivotal for preserving cellular homeostasis in response to physiological changes. Notably, disrupted circadian rhythms are observed in cases of impaired ISR signaling. In this work, we examine the potential interplay between the central circadian system and the ISR, mainly through the SCN and HPA axis. We introduce a semimechanistic mathematical model to delineate SCN's capacity for indirectly perceiving physiological stress through glucocorticoid-mediated feedback from the HPA axis and orchestrating a cellular response via the ISR mechanism. Key components of our investigation include evaluating general control nonderepressible 2 (GCN2) expression in the SCN, the effect of physiological stress stimuli on the HPA axis, and the interconnected feedback between the HPA and SCN. Simulation revealed a critical role for GCN2 in linking ISR with circadian rhythms. Experimental findings have demonstrated that a Gcn2 deletion in mice leads to rapid re-entrainment of the circadian clock following jetlag as well as to an elongation of the circadian period. These phenomena are well replicated by our model, which suggests that both the swift re-entrainment and prolonged period can be ascribed to a reduced robustness in neuronal oscillators. Our model also offers insights into phase shifts induced by acute physiological stress and the alignment/misalignment of physiological stress with external light-dark cues. Such understanding aids in strategizing responses to stressful events, such as nutritional status changes and jetlag.NEW & NOTEWORTHY This study is the first theoretical work to investigate the complex interaction between integrated stress response (ISR) sensing and central circadian rhythm regulation, encompassing the suprachiasmatic nucleus (SCN) and hypothalamus-pituitary-adrenal (HPA) axis. The findings carry implications for the development of dietary or pharmacological interventions aimed at facilitating recovery from stressful events, such as jetlag. Moreover, they provide promising prospects for potential therapeutic interventions that target circadian rhythm disruption and various stress-related disorders.

Chronic Cannabis users exhibit altered oscillatory dynamics and functional connectivity serving visuospatial processing.

Cannabis is the most widely used psychoactive drug in the United States. While multiple studies have associated acute cannabis consumption with alterations in cognitive function (e.g., visual and spatial attention), far less is known regarding the effects of chronic consumption on the neural dynamics supporting these cognitive functions. We used magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 44 regular cannabis users and 53 demographically matched non-user controls. To examine the effects of chronic cannabis use on the oscillatory dynamics underlying visuospatial processing, neural responses were imaged using a time-frequency resolved beamformer and compared across groups. Neuronal oscillations serving visuospatial processing were identified in the theta (4-8 Hz), alpha (8-14 Hz), and gamma range (56-76 Hz), and these were imaged and examined for group differences. Our key results indicated that users exhibited weaker theta oscillations in occipital and cerebellar regions and weaker gamma responses in the left temporal cortices compared to non-users. Lastly, alpha oscillations did not differ, but alpha connectivity among higher-order attention areas was weaker in cannabis users relative to non-users and correlated with performance. Overall, these results suggest that chronic cannabis users have alterations in the oscillatory dynamics and neural connectivity serving visuospatial attention. Such alterations were observed across multiple cortical areas critical for higher-order processing and may reflect compensatory activity and/or the initial emergence of aberrant dynamics. Future work is needed to fully understand the implications of altered multispectral oscillations and neural connectivity in cannabis users.

Investigating the effect of pregabalin on neuronal development using ultrashort self-assembling peptides: Assessing 3D neuronal cultures with high throughput robotic 3D bioprinting

Pregabalin is a widely prescribed drug for various neurological disorders, yet its effects on embryonic cortical neuron development when given to pregnant women remain inadequately explored. In this study, we employed advanced three-dimensional (3D) culturing and in-house developed high-throughput robotic 3D bioprinting technologies to evaluate their potential in neuropharmacology applications, using pregabalin as a model compound. Using a robotic 3D bioprinter and tetrameric IIZK peptide hydrogel as bioink, we created constructs with pregabalin-treated and untreated primary mouse embryonic cortical neurons. This setup allowed us to study the drug&amp;rsquo;s effects on cell viability, expression of neuronal markers, and neuron development. Our comparative analysis between 2D and 3D peptide-based cell culture models revealed that at a therapeutic concentration of 10 &amp;mu;M, pregabalin does not affect neuronal viability or the morphogenesis of cortical neurons. However, it significantly alters adenosine triphosphate (ATP) release, suggesting potential disruptions in mitochondrial function. Moreover, gene expression analysis of key genes involved in the development of the forebrain and the differentiation and maturation of neurons revealed significant alterations, including the downregulation of Dlx2, Nhlh2, Otp, and Gad67. These findings, together with observed alterations in neuronal activity and oscillations, emphasize the complex impact of pregabalin on neuronal development and function. They highlight the necessity for comprehensive clinical evaluations of its use during pregnancy. Furthermore, our research demonstrates the feasibility and value of integrating 3D cultures with high-throughput 3D bioprinting in neuropharmacology, opening new avenues for investigating drug effects on neuronal development and function, and contributing to safer clinical practices.

TransPhoM-DS Study Grant Report: Rationale and Protocol for Investigating the Efficacy of Low-Power Transcranial Photobiomodulation on Language, Executive Function, Attention, and Memory in Down Syndrome

Down syndrome (DS) is the leading genetic cause of intellectual disability globally, affecting about 1 in every 800 births. Individuals with DS often face various neuropsychiatric conditions alongside intellectual disabilities due to altered brain development. Despite the diverse phenotypic expressions of DS, typical physical characteristics frequently influence language development and acquisition. EEG studies have identified abnormal oscillatory patterns in individuals with DS. Emerging interventions targeting the enhancement of gamma (40 Hz) neuronal oscillations show potential for improving brain electrical activity and cognitive functions in this population. However, effective cognitive interventions for DS remain scarce. Extensive research indicates that transcranial photobiomodulation (t-PBM) with near-infrared (NIR) light can penetrate deeply into the cerebral cortex, modulate cortical excitability, and enhance cerebral perfusion and oxygenation. Furthermore, t-PBM has been shown to improve cognitive functions such as language, attention, inhibition, learning, and memory, including working memory. This study presents the rationale and design of an ongoing randomized, sham-controlled clinical trial aimed at assessing the effectiveness of t-PBM using NIR light in enhancing the language abilities of individuals with DS.

A memristive circuit for self-organized network topology formation based on guided axon growth

Circuit implementations of neuronal networks so far have been focusing on synaptic weight changes as network growth principles. Besides these weight changes, however, it is also useful to incorporate additional network growth principles such as guided axon growth and pruning. These allow for dynamical signal delays and a higher degree of self-organization, and can thus lead to novel circuit design principles. In this work we develop an ideal, bio-inspired electrical circuit mimicking growth and pruning controlled by guidance cues. The circuit is based on memristively coupled neuronal oscillators. As coupling element, we use memsensors consisting of a general sensor, two gradient sensors, and two memristors. The oscillators and memsensors are arranged in a grid structure, where oscillators and memsensors realize nodes and edges, respectively. This allows for arbitrary 2D growth scenarios with axon growth controlled by guidance cues. Simulation results show that the circuit successfully mimics a biological example in which two neurons initially grow towards two target neurons, where undesired connections are pruned later on.

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia.Graphical

Mesenchymal Stem Cells Promote an Increase in Neuronal Oscillation via Glutamate Tonic Release

Mesenchymal stromal cells (MSCs) hold therapeutic potential for neurological disorders, but their impact on neuronal activity remains unclear. We investigated the effects of SB623 cells (Notch-1 intracellular domain-transfected MSCs) and parental MSCs on human induced pluripotent stem cell (iPSC)-derived neurons using multi-electrode arrays. SB623 cells significantly increased neuronal activity and oscillation in a dose-dependent manner, surpassing astrocytes in promoting network bursts. Strikingly, glutamatergic neurons showed a rapid increase in activity and bursts compared to GABAergic neurons, suggesting glutamate release from SB623 cells. We confirmed this by finding high glutamate levels in SB623 cell conditioned medium, which were reduced by glutaminase inhibition. Glutamate release was further implicated by the reduced excitability in co-cultures with astrocytes, known glutamate scavengers. Our findings reveal a novel mechanism for MSCs: promoting neuronal activity and network formation through tonic glutamate release, with potential implications for MSC-based therapies.

Application of HFO and scaling analysis of neuronal oscillations in the presurgical evaluation of focal epilepsy

PurposeTo explore the utility of high frequency oscillations (HFO) and long-range temporal correlations (LRTCs) in preoperative assessment of epilepsy. MethodsMEG ripples were detected in 59 drug-resistant epilepsy patients, comprising 5 with parietal lobe epilepsy (PLE), 21 with frontal lobe epilepsy (FLE), 14 with lateral temporal lobe epilepsy (LTLE), and 19 with mesial temporal lobe epilepsy (MTLE) to identify the epileptogenic zone (EZ). The results were compared with clinical MEG reports and resection area. Subsequently, LRTCs were quantified at the source-level by detrended fluctuation analysis (DFA) and life/waiting -time at 5 bands for 90 cerebral cortex regions. The brain regions with larger DFA exponents and standardized life-waiting biomarkers were compared with the resection results. ResultsCompared to MEG sensor-level data, ripple sources were more frequently localized within the resection area. Moreover, source-level analysis revealed a higher proportion of DFA exponents and life-waiting biomarkers with relatively higher rankings, primarily distributed within the resection area (p<0.01). Moreover, these two LRCT indices across five distinct frequency bands correlated with EZ. ConclusionHFO and source-level LRTCs are correlated with EZ. Integrating HFO and LRTCs may be an effective approach for presurgical evaluation of epilepsy.