Biology subjects

Weinstein, A.

Publications and source records attributed to Weinstein, A..

10 recordsLinked to original sources

bioRxiv2026

Pediatric traumatic brain injury elicits acute neuroinflammation and long-term changes in social, cognitive, and decision-making behaviors in male and female rats

Traumatic brain injury (TBI) is one of the leading causes of emergency room visits in children under 10. Children are potentially more vulnerable to the adverse effects of TBI, given that their brains are still developing at the time of injury. Indeed, early life TBI has been linked to cognitive, social, and mood-related impairments later in life. The neuroimmune system has been implicated in adult TBI mechanisms and plays numerous key roles in brain development, making it an interesting candidate for linking pediatric TBI and prolonged behavioral alterations. Here we establish a rat model of mild pediatric TBI to investigate the relationship between early life TBI, acute responses of neuroimmune cells, and chronic behavioral dysregulation. At postnatal day 15, which is roughly equivalent to toddler age, male and female rat pups received a TBI via lateral fluid percussion injury. At 3 days post injury, TBI increased microglia and astrocyte coverage locally in the Perilesional Cortex but not in more distant corticolimbic regions. However, the hippocampus and prefrontal cortex did exhibit increased expression of the phagocytic marker CD68 in microglia, suggesting widespread glial activation even in the absence of gross coverage change. TBI also impacted mast cells, early-response innate immune cells, increasing their number and degranulation in multiple regions. In the juvenile and early adult periods, TBI impaired cognitive function, reduced sociability, and increased avoidance, with no change in anxiety-like behavior. Later in adulthood, TBI continued to impact cognitive behavior, increasing risky decision-making and impairing optimization months after injury. Together, these results suggest that pediatric TBI causes lasting cognitive and social dysregulation, possibly via acute neuroimmune alterations following injury at a critical period of brain development.

neuroscience↗

bioRxiv2026

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzheimers disease (AD) and Parkinsons disease (PD). To this end, key spectral features were first identified using explainable machine learning, and their temporal dynamics were then examined to characterize variability patterns and statistical properties. EEG recordings were segmented into non-overlapping 4-s epochs, from which spectral features based on relative band power and spectral entropy were extracted. Random Forest classifiers were trained to discriminate individual subjects with AD and PD from healthy controls (HC) using a Leave-One-Subject-Out Cross-Validation (LOSOCV) strategy. The most discriminative spectral features and the directionality of their contributions were identified through a SHAP-based explainable analysis. Subsequently, the temporal dynamics of the key features were analyzed to characterize disease fingerprints in terms of variability at both inter-subject and intra-subject levels and their distributional profiles. Our results confirmed spectral slowing in both disorders and revealed disorder-specific differences in the dominant spectral markers: the theta/alpha ratio was the most influential feature for AD, whereas mean relative theta power was the primary feature for PD discrimination. We show that increased variability in key spectral features is a distinguishing signature of AD and PD, with disease groups exhibiting greater inter-subject heterogeneity and higher intra-subject temporal variability than HC. Moreover, the key features showed heavy-tailed behavior, for which a lognormal model provided a plausible fit across groups. We conclude that this EEG-based characterization provides a meaningful avenue for tracking deviations from healthy neural activity.

neuroscience↗

bioRxiv2025

Frequency-Dependent Neurostimulation in Whole Brain Network models

BackgroundBrain stimulation at specific frequencies has been shown to have therapeutic effects for certain neurological disorders. However, it remains unclear how these oscillatory inputs interact with neuronal dynamics and what drives this frequency selectivity. Here, we propose that this is achieved by targeting specific brain circuits through frequency-selective network entrainment. MethodsTo demonstrate that this type of entrainment can occur in the brain connectome structure, we use a minimal physics-based model of coupled oscillators that preserves only the essential structural connectivity of real brain networks. Using this model, we test how periodic stimulation influences network oscillatory dynamics, identifying sub-networks that synchronize with specific stimulation frequencies. Furthermore, we validate the suitability of the model to reproduce well-known frequency-selective entrainment using visual and auditory periodic stimulation. ResultsOur model reproduces selective network entrainment, whereby distinct stimulation frequencies favor the synchrony of different sub-networks. Crucially, entrainment exhibits a physics-predicted inverse relationship with frequency: lower-frequency stimulation favors broader synchrony across the network, while higher-frequency inputs produce spatially confined effects. These patterns emerge purely from network structure and oscillatory physics, independent of biological details. ConclusionThis physics-based approach elucidates the fundamental mechanistic principles governing frequency-selective brain network stimulation. Our minimal model demonstrates that selective entrainment of specific subnetworks can be achieved through frequency selection alone, largely determined by network structure and oscillatory dynamics. These findings provide a theoretical foundation for understanding how network architecture determines stimulation selectivity, supporting the development of principled approaches to targeted neuromodulation. Author summaryIn this work, we explore how external rhythmic stimulation interacts with the brains own rhythms. We use a physics-based model that represents the brain as a network of interconnected oscillators, linked according to the human connectome. Through this model, we show that stimulation effects are not limited to the targeted region but spread across the network, depending on the stimulation frequency, its spatial location and the underlying network dynamics. Our results reveal that low-frequency rhythms promote large-scale synchrony, while high-frequency rhythms engage more localized areas, all emerging from the brains structural connections and communication delays. The simulated cortical activation exhibits a frequency-selective profile that aligns strongly with that estimated from sensory-evoked electroencephalographic activity. This agreement suggests that differences between individuals in how their brain networks operate may explain why stimulation affects each person differently. Our findings offer a mechanistic framework for designing personalized stimulation protocols, illustrating how computational modeling can guide the optimization of noninvasive brain stimulation in both research and clinical contexts.

bioengineering↗

bioRxiv2025

Effects of Electrocardiograms QRS Detection Algorithms in Heart Rate Variability Metrics

Heart Rate Variability (HRV) is a marker used for assessing autonomic nervous system function, derived from the timing between R-peaks in electrocardiogram (ECG) signals. Accurate detection of QRS complexes is essential for reliable HRV computation. While many R-wave detection algorithms exist, their impact on the accuracy of HRV metrics remains underexplored. This study addresses this gap by assessing how QRS detection errors affect HRV analysis across different algorithms and recording setups. We evaluated eight widely used QRS detectors using ECG recordings from 25 healthy participants under rest, cognitive load, and physical activity conditions. Two acquisition setups were considered: "chest strap" and "loose cables." We used the manually annotated R-peaks to calculate the ground-truth HRV metric values. The relationship between the detector performance and HRV errors was evaluated for 11 metrics using the concordance correlation coefficient (CCC). Results showed significant variability in detector performance across algorithms and setups. No single QRS detection algorithm outperformed across all scenarios. Loose cable recordings yielded higher CCC values than chest straps, particularly for MeanNN and LF power. These findings highlight the critical role of QRS detector selection and signal acquisition conditions in HRV analysis. They underscore the need for context-specific benchmarking, particularly for wearable and ambulatory applications where signal quality can vary. Ultimately, this study offers practical recommendations for clinicians and researchers on selecting QRS detection algorithms that best align with their specific analytical objectives and recording conditions.

bioengineering↗

bioRxiv2024

Frequent transitions in self-assembly across the evolution of a central metabolic enzyme

Many enzymes assemble into homomeric protein complexes comprising multiple copies of one protein. Because structural form is usually assumed to follow function in biochemistry, these assemblies are thought to evolve because they provide some functional advantage. In many cases, however, no specific advantage is known and, in some cases, quaternary structure varies among orthologs. This has led to the proposition that self-assembly may instead vary neutrally within protein families. The extent of such variation has been difficult to ascertain because quaternary structure has until recently been difficult to measure on large scales. Here, we employ mass photometry, phylogenetics, and structural biology to interrogate the evolution of homo-oligomeric assembly across the entire phylogeny of prokaryotic citrate synthases - an enzyme with a highly conserved function. We discover a menagerie of different assembly types that come and go over the course of evolution, including cases of parallel evolution and reversions from complex to simple assemblies. Functional experiments in vitro and in vivo indicate that evolutionary transitions between different assemblies do not strongly influence enzyme catalysis. Our work suggests that enzymes can wander relatively freely through a large space of possible assemblies and demonstrates the power of characterizing structure-function relationships across entire phylogenies.

evolutionary biology↗

bioRxiv2024

Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome-coupled oscillators model

Large-scale brain models with biophysical or biophysically inspired parameters generate brain-like dynamics with multi-state metastability. Multi-state metastability reflects the capacity of the brain to transition between different network configurations and cognitive states in response to changing environments or tasks, thus relating to cognitive flexibility. To study this phenomenon, we used a Kuramoto network of oscillators corresponding to a human brain atlas of 90 nodes, each with an intrinsic frequency of 40 Hz. The networks nodes were interconnected based on the structural connectivity strengths and delays found in the human brain. We identified global coupling and delay scale parameters corresponding to maximum spectral entropy, a proxy for maximal multi-state metastability. At this point, we show that multiple coherent (functional) sub-networks spontaneously emerge across multiple oscillatory modes, and persist in time for periods between 140 and 4389 ms. Most nodes in the network exhibit broad frequency spectra away from their intrinsic frequency, and switch between modes, in a manner similar to that reported in empirical resting-state neuroimaging data. We suggest that the obtained dynamics at maximum metastability is a suitable model of the awake brain. Further, we show that global coupling and delay scale parameters away from maximum metastability yield dynamical features similar to other brain states such as sleep and anesthesia. Therefore, spectral entropy also correlates with wakefulness and the synchrony of functional networks.

neuroscience↗

bioRxiv2023

A High-Resolution LED Display for Steady State Visual Stimulation: Customized, Affordable, and Open Source

Visually evoked steady-state potentials (SSVEPs) are neural responses elicited by visual stimuli oscillating at specific frequencies. In this study, we introduce a novel LED display system designed specifically for steady-state visual stimulation, offering precise control over visual stimulus parameters, including frequency resolution, luminance, and the ability to control the phase at the end of the stimulation. The LED display provides a personalized, modular, and affordable option for experimental setups. Based on the Teensy 3.2 board, the display utilizes Direct Digital Synthesis and Pulse Width Modulation techniques to control the LEDs. Its performance is validated through four experiments: the first two measure LED light intensities directly, while the last two assess the displays impact on EEG recordings. The results demonstrate that the display can deliver a stimulus suitable for generating SSVEPs with the desired frequency and phase resolution. We provide comprehensive documentation, including all necessary codes and electrical diagrams, as an open-source resource. This facilitates the replication and adaptation of the system for specific experimental requirements, enhancing its potential for widespread use in the field of neuroscience.

bioinformatics↗

bioRxiv2022

Acoustic and Neurophysiological Aspects of Lombard Effect

PurposeThis study aims to describe variations in acoustic and electroencephalography measures when speaking in the presence of background noise (Lombard effect) in participants with typical voice and normal hearing. MethodTwenty-one participants with typical voices and normal hearing uttered simple vocal tasks in three sequential background conditions: Baseline (in quiet), Lombard (in noise), and Recovery (five minutes after removing the noise). Acoustic and electroencephalography signals were recorded in all conditions. The noise used in the Lombard condition consisted of speech-shaped noise at 80 dB SPL sent by headphones. Acoustic measure, and ERP responses were analyzed. ResultsDuring the Lombard condition, the participants increased the intensity of their voice, accompanied by an increase in CPP, and a decrease in H1-H2. The cortical response was characterized by the increased N1-P2 complex amplitude of the ERP elicited by the subjects own vocalizations in noise, The source localization showed neural activities in frontal and temporal cortical regions. ConclusionsThe variation in acoustic measures due to the Lombard Effect could be modulated by temporal, and cortical regions.

neuroscience↗

bioRxiv2018

Joint single cell DNA-Seq and RNA-Seq of gastric cancer reveals subclonal signatures of genomic instability and gene expression

Sequencing the genomes of individual cancer cells provides the highest resolution of intratumoral heterogeneity. To enable high throughput single cell DNA-Seq across thousands of individual cells per sample, we developed a droplet-based, automated partitioning technology for whole genome sequencing. We applied this approach on a set of gastric cancer cell lines and a primary gastric tumor. In parallel, we conducted a separate single cell RNA-Seq analysis on these same cancers and used copy number to compare results. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection - where the percentage of replicating cells per clone is higher than expected - indicating ongoing tumor evolution. Our study demonstrates that joining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology. SIGNIFICANCEWe conducted a massively parallel DNA sequencing analysis on a set of gastric cancer cell lines and a primary gastric tumor in combination with a joint single cell RNA-Seq analysis. This joint study, covering thousands of single cell genomes and transcriptomes, revealed extensive cellular diversity based on distinct copy number changes, numerous subclonal populations and in the case of the primary tumor, subclonal gene expression signatures. We found genomic evidence of positive selection where the percentage of replicating cells per clone is higher than expected indicating ongoing tumor evolution. Our study demonstrates that combining single cell genomic DNA and transcriptomic features provides novel insights into cancer heterogeneity and biology.

genomics↗

bioRxiv2018

Estimation of auditory steady-state responses based on the averaging of independent EEG epochs

The amplitude of the auditory steady-state responses (ASSRs) generated in the brainstem exponentially decreases over the averaging of subsequent EEG epochs. This behavior is partially due to the adaptation of the auditory response to the continuous and monotonous stimulation. We analyzed the potential clinical relevance of the ASSR adaptation. Specifically, we compare the ASSR amplitude computed in two conditions: (1) when the auditory responses -embedded in the EEG epochs that are averaged in the estimation procedure- are influenced by the previous stimulation; and (2) when they are independent of the previous stimulation. ASSR were elicited in eight anesthetized adult rats by 8-kHz tones, modulated in amplitude at 115 Hz. ASSR amplitudes were computed using three averaging methods (standard, weighted and sorted averaging). We evaluated the ASSR amplitude as a function of sub-set of epochs selected for the averaging and the improvement in the ASSR detection resulting from averaging independent epochs. Due to adaptation, the ASSR amplitude computed by averaging dependent EEG epochs relied upon the averaging method. Lower ASSR amplitudes were obtained as EEG segments containing unadapted responses were systematically excluded from the averaging. In the absence of EEG artifacts, the ASSR amplitudes did not depend on the averaging method when they were computed from independent EEG epochs. The amplitude of independent ASSRs were up to 35% higher than those obtained by processing dependent EEG segments. Extracting the ASSR amplitude from independent epochs halved the number of EEG segments needed to be averaged to achieve the maximum detection rate of the response. Acquisition paradigm based on a discrete acoustic stimulation (in which segments of AM-sounds of several seconds in length are presented after a given inter stimulus interval), in combination with appropriated averaging methods might increase the accuracy of audiological tests based on ASSRs.

neuroscience↗