Neural Activity Research Articles

TWISP: A Transgenic Worm for Interrogating Signal Propagation in C. elegans.

Genetically encoded optical indicators and actuators of neural activity allow for all-optical investigations of signaling in the nervous system. But commonly used indicators, actuators and expression strategies are poorly suited for systematic measurements of signal propagation at brain scale and cellular resolution. Large scale measurements of the brain require indicators and actuators with compatible excitation spectra to avoid optical crosstalk. They must be highly expressed in every neuron but at the same time avoid lethality and permit the animal to reach adulthood. Their expression must also be compatible with additional fluorescent labels to locate and identify neurons, such as those in the NeuroPAL cell identification system. We present TWISP, a Transgenic Worm for Interrogating Signal Propagation, that addresses these needs and enables optical measurements of evoked calcium activity at brain scale and cellular resolution in the nervous system of the nematode Caenorhabditis elegans. We express in every neuron a non-conventional optical actuator, the gustatory receptor homolog GUR-3 + PRDX-2 under the control of a drug-inducible system QF + hGR, and calcium indicator GCAMP6 s, in a background with additional fluorophores of the NeuroPAL cell ID system. We show that this combination, but not others tested, avoids optical-crosstalk, creates strong expression in the adult, and generates stable transgenic lines for systematic measurements of signal propagation in the worm brain.

Inhibitory subpopulations in preBötzinger Complex play distinct roles in modulating inspiratory rhythm and pattern.

Inhibitory neurons embedded within mammalian neural circuits shape breathing, walking, and other rhythmic motor behaviors. At the core of the neural circuit controlling breathing is the preBötzinger Complex (preBötC), where GABAergic (GAD1/2+) and glycinergic (GlyT2+) neurons are functionally and anatomically intercalated among glutamatergic Dbx1-derived (Dbx1+) neurons that generate rhythmic inspiratory drive. The roles of these preBötC inhibitory neurons in breathing remain unclear. We first characterized the spatial distribution of molecularly-defined preBötC inhibitory subpopulations in male and female neonatal double reporter mice expressing either tdTomato or EGFP in GlyT2+, GAD1+, or GAD2+ neurons. We found that the majority of preBötC inhibitory neurons expressed both GlyT2 and GAD2 while a much smaller subpopulation also expressed GAD1. To determine the functional role of these subpopulations, we used holographic photostimulation, a patterned illumination technique, in rhythmically active medullary slices from neonatal Dbx1tdTomato;GlyT2EGFP and Dbx1tdTomato;GAD1EGFP double reporter mice of either sex. Stimulation of 4 or 8 preBötC GlyT2+ neurons during endogenous rhythm prolonged the interburst interval in a phase-dependent manner and increased the latency to burst initiation when bursts were evoked by stimulation of Dbx1+ neurons. In contrast, stimulation of 4 or 8 preBötC GAD1+ neurons did not affect interburst interval or latency to burst initiation. Instead, photoactivation of GAD1+ neurons during the inspiratory burst prolonged endogenous and evoked burst duration and decreased evoked burst amplitude. We conclude that GlyT2+/GAD2+ neurons modulate breathing rhythm by delaying burst initiation while a smaller GAD1+ subpopulation shapes inspiratory patterning by altering burst duration and amplitude.Significance Statement Inhibition is critical for control of rhythmic motor behaviors, such as breathing, walking, and chewing. We reveal functional differences among embedded preBötC inhibitory micro-cir-cuits that contribute to excitation-inhibition balance governing distinct processes in the breathing motor program. We develop approaches for high throughput analysis of spatial distributions of neurons in tissue and for dynamic functional manipulations via an important extension of holographic photostimulation to sequences of patterns that more closely mimics physiological neural activity. Our study challenges the current understanding of the role of inhibition in breathing and provides novel insights into specific roles of inhibitory neurons in neural circuits controlling breathing that are applicable to other rhythmic motor behaviors in health and disease.

The impact of stress on the risk decision-making process.

The effect of stress on risk-taking or risk-averse behavior in decision-making has been inconclusive in previous research, with few studies revealing the underlying neural mechanisms. This study employed event-related potentials technique and combined a social cold pressor assessment test with a mental arithmetic task to induce stress responses, aiming to investigate the influence of exogenous stress on the risk decision-making process. Stress induction results indicated that, in addition to raising heart rate and blood pressure, stress responses were accompanied by enhanced negative emotions, diminished positive emotions, and alterations in neural activity. The outcomes of risk decision-making showed that stress did not significantly affect risk preference or time of choice but did reduce the feedback-related negativity/reward positivity, with a particularly significant effect observed for large outcomes. Stress also altered the amplitude of the P3 component, with stress decreasing the P3 value for winning outcomes relative to losing outcomes. The study suggests that understanding how stress affects risk preference should consider the emotional valence induced by stress. Contrary to the reward sensitivity hypothesis, stress weakened reward sensitivity. Stress led to changes in the allocation of cognitive resources for outcome evaluation: compared to negative outcomes, stress reduced cognitive resources for positive outcomes, which might be related to the enhanced negative emotions induced by stress. The study highlights the importance of focusing on the subjective emotional experience induced by stress in future research on stress and risk decision-making.

Noise-induced hearing loss alters potassium-chloride cotransporter KCC2 and GABA inhibition in the auditory centers

Homeostatic plasticity, the ability of neurons to maintain their averaged activity constant around a set point value, is thought to account for the central hyperactivity after hearing loss. Here, we investigated the putative role of GABAergic neurotransmission in this mechanism after a noise-induced hearing loss larger than 50 dB in high frequencies in guinea pigs. The effect of GABAergic inhibition is linked to the normal functioning of K + –Cl– co-transporter isoform 2 (KCC2) which maintains a low intracellular concentration of chloride. The expression of membrane KCC2 were investigated before and after noise trauma in the ventral and dorsal cochlear nucleus (VCN and DCN, respectively) and in the inferior colliculus (IC). Moreover, the effect of gabazine (GBZ), a GABA antagonist, was also studied on the neural activity in IC. We show that KCC2 is downregulated in VCN, DCN and IC 3 days after noise trauma, and in DCN and IC 30 days after the trauma. As expected, GBZ application in the IC of control animals resulted in an increase of spontaneous and stimulus-evoked activity. In the noise exposed animals, on the other hand, GBZ application decreased the stimulus-evoked activity in IC neurons. The functional implications of these central changes are discussed.

Brain organoids engineered to give rise to glia and neural networks after 90 days in culture exhibit human-specific proteoforms

Human brain organoids are emerging as translationally relevant models for the study of human brain health and disease. However, it remains to be shown whether human-specific protein processing is conserved in human brain organoids. Herein, we demonstrate that cell fate and composition of unguided brain organoids are dictated by culture conditions during embryoid body formation, and that culture conditions at this stage can be optimized to result in the presence of glia-associated proteins and neural network activity as early as three-months in vitro. Under these optimized conditions, unguided brain organoids generated from induced pluripotent stem cells (iPSCs) derived from male–female siblings are similar in growth rate, size, and total protein content, and exhibit minimal batch-to-batch variability in cell composition and metabolism. A comparison of neuronal, microglial, and macroglial (astrocyte and oligodendrocyte) markers reveals that profiles in these brain organoids are more similar to autopsied human cortical and cerebellar profiles than to those in mouse cortical samples, providing the first demonstration that human-specific protein processing is largely conserved in unguided brain organoids. Thus, our organoid protocol provides four major cell types that appear to process proteins in a manner very similar to the human brain, and they do so in half the time required by other protocols. This unique copy of the human brain and basic characteristics lay the foundation for future studies aiming to investigate human brain-specific protein patterning (e.g., isoforms, splice variants) as well as modulate glial and neuronal processes in an in situ-like environment.

Neural Control of Naturalistic Behavior Choices

In the natural world, animals make decisions on an ongoing basis, continuously selecting which action to undertake next. In the lab, however, the neural bases of decision processes have mostly been studied using artificial trial structures. New experimental tools based on the genetic toolkit of model organisms now make it experimentally feasible to monitor and manipulate neural activity in small subsets of neurons during naturalistic behaviors. We thus propose a new approach to investigating decision processes, termed reverse neuroethology. In this approach, experimenters select animal models based on experimental accessibility and then utilize cutting-edge tools such as connectomes and genetically encoded reagents to analyze the flow of information through an animal's nervous system during naturalistic choice behaviors. We describe how the reverse neuroethology strategy has been applied to understand the neural underpinnings of innate, rapid decision making, with a focus on defensive behavioral choices in the vinegar fly Drosophila melanogaster.

Counterfactual thinking induces different neural patterns of memory modification in anxious individuals

Episodic counterfactual thinking (eCFT) is the process of mentally simulating alternate versions of experiences, which confers new phenomenological properties to the original memory and may be a useful therapeutic target for trait anxiety. However, it remains unclear how the neural representations of a memory change during eCFT. We hypothesized that eCFT-induced memory modification is associated with changes to the neural pattern of a memory primarily within the default mode network, moderated by dispositional anxiety levels. We tested this proposal by examining the representational dynamics of eCFT for 39 participants varying in trait anxiety. During eCFT, lateral parietal regions showed progressively more distinct activity patterns, whereas medial frontal neural activity patterns became more similar to those of the original memory. Neural pattern similarity in many default mode network regions was moderated by trait anxiety, where highly anxious individuals exhibited more generalized representations for upward eCFT (better counterfactual outcomes), but more distinct representations for downward eCFT (worse counterfactual outcomes). Our findings illustrate the efficacy of examining eCFT-based memory modification via neural pattern similarity, as well as the intricate interplay between trait anxiety and eCFT generation.

Task-Dependent Brain Activity in Generalized Anxiety Disorder Determined By Bayesian Spatio-Temporal Single Case Model

Introduction: Data obtained from functional magnetic resonance imaging (fMRI) have a complex structure. Considering the special features of this type of data in analyses is of particular importance. Previous studies on generalized anxiety disorder (GAD) as a prevalent mental disorder using functional neuroimaging have had conflicting results. In this study, we apply a Bayesian spatiotemporal model to this type of data which considers both spatial and temporal dependence among regions which is one of the most essential features to consider. Methods: In this single-subject study, we analyze data from a patient with GAD and a healthy participant. Both participants are 24-year-old women who are assigned an emotion reactivity task (matching neutral and negative facial expressions) inside a scanner. The spatial Bayesian variable selection method is used to detect blood oxygen level-dependent activation in fMRI data. Results: Activation areas in neutral and negative facial expressions are provided for both participants by posterior probability map. The results of our study show a greater level of activity in the GAD participant in comparison to the healthy participant in responding to the negative matching task. Conclusion: the GAD patient showed more neural activity in response to negative facial expressions than the healthy participant in brain regions related to emotional response in the areas of the frontal Pole, middle frontal gyrus, insular cortex, and frontal orbital cortex. Moreover, the inferior frontal gyrus in the patient with GAD showed more reaction to negative emotional stimuli.

Distributed and dynamical communication: a mechanism for flexible cortico-cortical interactions and its functional roles in visual attention

Perceptual and cognitive processing relies on flexible communication among cortical areas; however, the underlying neural mechanism remains unclear. Here we report a mechanism based on the realistic spatiotemporal dynamics of propagating wave patterns in neural population activity. Using a biophysically plausible, multiarea spiking neural circuit model, we demonstrate that these wave patterns, characterized by their rich and complex dynamics, can account for a wide variety of empirically observed neural processes. The coordinated interactions of these wave patterns give rise to distributed and dynamic communication (DDC) that enables flexible and rapid routing of neural activity across cortical areas. We elucidate how DDC unifies the previously proposed oscillation synchronization-based and subspace-based views of interareal communication, offering experimentally testable predictions that we validate through the analysis of Allen Institute Neuropixels data. Furthermore, we demonstrate that DDC can be effectively modulated during attention tasks through the interplay of neuromodulators and cortical feedback loops. This modulation process explains many neural effects of attention, underscoring the fundamental functional role of DDC in cognition.

Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt

In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.

Toward a brighter constellation: multiorgan neuroimaging of neural and vascular dynamics in the spinal cord and brain.

Pain comprises a complex interaction between motor action and somatosensation that is dependent on dynamic interactions between the brain and spinal cord. This makes understanding pain particularly challenging as it involves rich interactions between many circuits (e.g., neural and vascular) and signaling cascades throughout the body. As such, experimentation on a single region may lead to an incomplete and potentially incorrect understanding of crucial underlying mechanisms. We aimed to develop and validate tools to enable detailed and extended observation of neural and vascular activity in the brain and spinal cord. The first key set of innovations was targeted to developing novel imaging hardware that addresses the many challenges of multisite imaging. The second key set of innovations was targeted to enabling bioluminescent (BL) imaging, as this approach can address limitations of fluorescent microscopy including photobleaching, phototoxicity, and decreased resolution due to scattering of excitation signals. We designed 3D-printed brain and spinal cord implants to enable effective surgical implantations and optical access with wearable miniscopes or an open window (e.g., for one- or two-photon microscopy or optogenetic stimulation). We also tested the viability for BL imaging and developed a novel modified miniscope optimized for these signals (BLmini). We describe "universal" implants for acute and chronic simultaneous brain-spinal cord imaging and optical stimulation. We further describe successful imaging of BL signals in both foci and a new miniscope, the "BLmini," which has reduced weight, cost, and form-factor relative to standard wearable miniscopes. The combination of 3D-printed implants, advanced imaging tools, and bioluminescence imaging techniques offers a coalition of methods for understanding spinal cord-brain interactions. Our work has the potential for use in future research into neuropathic pain and other sensory disorders and motor behavior.

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson’s disease

Objective. Beta triggered closed–loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson’s disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia–thalamus–cortical circuit. Approach. A dynamic basal ganglia–thalamus–cortical mean–field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional–integral–differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean–field model. Main results. The Results show that the dynamic basal ganglia–thalamus–cortical mean–field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment. Significance. This work provides a new method of closed–loop DBS for multi-timescale beta power modulation with clinical constraints.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks

We present a novel set of quantitative measures for “likeness” (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative “blended” system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron’s rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

Carrier-frequency specific omission-related neural activity in ordered sound sequences is independent of omission-predictability.

Regularities in our surroundings lead to predictions about upcoming events. Previous research has shown that omitted sounds during otherwise regular tone sequences elicit frequency-specific neural activity related to the upcoming but omitted tone. We tested whether this neural response is depending on the unpredictability of the omission. Therefore, we recorded magnetencephalography (MEG) data while participants listened to ordered or random tone sequences with omissions occurring either ordered or randomly. Using multivariate pattern analysis shows that the frequency-specific neural pattern during omission within ordered tone sequences occurs independent of the regularity of the omissions. These results suggest that the auditory predictions based on sensory experiences are not immediately updated by violations of those expectations.

Memory retrieval effects as a function of differences in phenomenal experience.

Conscious experience and perception are restricted to a single perspective. Although evidence to suggest differences in phenomenal experience can produce observable differences in behavior, it is not well understood how these differences might influence memory. We used fMRI to scan n = 49 participants while they encoded and performed a recognition memory test for faces and words. We calculated a cognitive bias score reflecting individual participants' propensity toward either Visual Imagery or Internal Verbalization based on their responses to the Internal Representations Questionnaire (IRQ). Neither visual imagery nor internal verbalization scores were significantly correlated with memory performance. In the fMRI data, there were typical patterns of activation differences between words and faces during both encoding and retrieval. There was no effect of internal representation bias on fMRI activation during encoding. At retrieval, however, a bias toward visualization was positively correlated with memory-related activation for both words and faces in inferior occipital gyri. Further, there was a crossover interaction in a network of brain regions such that visualization bias was associated with greater activation for words and verbalization bias was associated with greater activation for faces, consistent with increased effort for non-preferred stimulus retrieval. These findings suggest that individual differences in cognitive representations affect neural activation across different types of stimuli, potentially affecting memory retrieval performance.

The application of neuronavigated rTMS of the supplementary motor area and rhythmic speech training for stuttering intervention.

Stuttering, a neurodevelopmental speech fluency disorder, is associated with intermittent disruptions of speech-motor control. Behavioural treatments for adults who stutter (AWS) concentrate on adopting speech patterns that enhance fluency, such as speaking rhythmically or prolonging speech sounds. However, maintaining these treatment benefits can be challenging. Neuroimaging studies suggest that supplementary motor area (SMA) which play a crucial role in speech initiation, planning and internal timing shows aberrant activation in speech production of AWS and may contribute to stuttering. Preliminary evidence suggests that brain stimulation may impact responsiveness to behavioural treatments. The present study aims to investigate whether excitatory repetitive transcranial magnetic stimulation (rTMS) of the SMA and rhythmic speech can consistently reduce stuttering severity across various measures. Ten self-identified Cantonese-speaking AWS participated in this double-blinded, sham-controlled clinical trial study (NCT05472181). The participants underwent 10 sessions of rhythmic speech training across two phases, combined with either neuronavigated rTMS or sham, with a 2-week washout period between phases. The stuttering severity was assessed through various outcome measures, including the percentage of syllables stuttered, self-perceived stuttering severity, and the brief version of Unhelpful Thoughts and Beliefs About Stuttering before and after each treatment phase. Results demonstrated improved speech fluency in various speaking contexts, with no significant difference between rTMS and sham conditions immediately and 1 week post-treatment. Notably, rTMS specifically led to less stuttering in tongue twister production (d = -0.70). Both treatment conditions effectively reduced self-perceived stuttering severity and negative thoughts and beliefs about stuttering. The findings of this study indicate that stimulating the SMA reduced stuttering, only in the production of tongue twisters that may require greater motor control and coordination. Furthermore, it indicates that rhythmic speech might help alleviate negative beliefs and anxiety related to stuttering. This research contributes to our understanding of neuromodulation in stuttering treatment and the role of the SMA in speech motor control and emphasises the need for more research on the potential benefits and limitations of applying rTMS in this condition. What is already known on the subject Behavioural treatments for adults who stutter concentrate on adopting speech patterns that enhance fluency, such as speaking rhythmically or prolonging speech sounds. However, maintaining these treatment benefits can be challenging. Neuroimaging studies indicate that aberrant neural activation in speech production regions, like the supplementary motor area (SMA), is involved in stuttering. The SMA plays a crucial role in initiating, planning, and sequencing motor behaviours. Preliminary evidence suggests that brain stimulation (e.g., transcranial direct current stimulation or transcranial magnetic stimulation) may impact responsiveness to behavioural treatments. What this paper adds to existing knowledge There is limited knowledge regarding the potential effects of stimulating the SMA to enhance speech fluency in people who stutter. Existing research primarily consists of single case studies that lack proper control conditions or involve only a single stimulation session. Due to their limited scope and power, these studies may not provide sufficient evidence. The current study expands upon existing research by investigating whether multiple sessions of repetitive transcranial magnetic stimulation over the SMA, combined with rhythmic speech, improve speech fluency in adults who stutter. Furthermore, it addresses the limitations of brain stimulation methods and proposes directions for future research. What are the potential or actual clinical implications of this work? This study implies that the stimulation of SMA reduced stuttering only in speaking contexts that may require greater motor control and coordination such as tongue twisters. Additionally, the research suggests that using rhythmic speech could potentially alleviate negative beliefs and anxiety associated with stuttering.

Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

Abnormal neural activation during negative emotion processing in anxiety disorders: a coordinate-based meta-analysis.

Abnormal neural activation during negative emotion processing in anxiety disorders: a coordinate-based meta-analysis.

Patterns of spontaneous neural activity associated with social communication abilities among infants and toddlers showing signs of autism.

Early disruptions to social communication development, including delays in joint attention and language, are among the earliest markers of autism spectrum disorder (autism, henceforth). Although social communication differences are a core feature of autism, there is marked heterogeneity in social communication-related development among infants and toddlers exhibiting autism symptoms. Neural markers of individual differences in joint attention and language abilities may provide important insight into heterogeneity in autism symptom expression during infancy and toddlerhood. This study examined patterns of spontaneous electroencephalography (EEG) activity associated with joint attention and language skills in 70 community-referred 12- to 23-month-olds with autism symptoms and elevated scores on an autism diagnostic instrument. Data-driven cluster-based permutation analyses revealed significant positive associations between relative alpha power (6-9Hz) and concurrent response to joint attention skills, receptive language, and expressive language abilities. Exploratory analyses also revealed significant negative associations between relative alpha power and measures of core autism features (i.e., social communication difficulties and restricted/repetitive behaviors). These findings shed light on the neural mechanisms underlying typical and atypical social communication development in emerging autism and provide a foundation for future work examining neural predictors of social communication growth and markers of intervention response.

LabVIEW based Mental Status Monitoring System for Cognitive Wellness

This paper delves into the analysis of various cognitive states by identifying the percentages of different types of brain waves, like alpha, beta, delta, and theta, present in an individual's EEG recording. These brain waves carry crucial information about an individual's cognitive health, and accurate readings are essential to classify and represent the signals accurately. To achieve this, the project aimed to develop a robust platform that utilizes LabVIEW software to obtain EEG signals and interpret cognitive health. The acquired data is meticulously preprocessed and filtered to obtain clean EEG signals, which are then subjected to several methods to determine the content of different brain waves present in the filtered EEG signals. Based on these values, the cognitive status of an individual can be accurately determined, providing valuable insights into their cognitive health. Understanding cognitive states through brain wave analysis has been an intriguing area of research, utilizing various electroencephalography (EEG) techniques to interpret neural activity. This paper aims to provide a detailed discussion on how cognitive states are mapped and interpreted based on brain waves. By exploring the characteristics of each brain wave frequency band, we elucidate their implications in cognitive processes such as attention, memory, and relaxation. This research aims to contribute in the advancement of cognitive neuroscience by consolidating current knowledge and identifying potential avenues for future research.