Biology subjects

Kramer, M. A.

Publications and source records attributed to Kramer, M. A..

15 recordsLinked to original sources

bioRxiv2026

Sleep spindle deficits in childhood absence epilepsy improve with antiseizure treatment and disease resolution

Childhood absence epilepsy (CAE), the most common childhood epilepsy syndrome, is associated with bursts of generalized thalamocortical spike-wave discharges and cognitive impairment, yet the physiological mechanisms linking disease activity, treatment, and cognition remain unclear. Sleep spindles, generated by thalamocortical circuits, are associated with cognitive function and may be disrupted by epileptic activity. Here, we retrospectively studied 87 EEGs from 53 children with active and resolved CAE and 87 age- and sex-matched controls to evaluate sleep spindle activity across disease states and its association with antiseizure medication (ASM) exposure. We found that children with active CAE had reduced spindle rates across all cortical regions compared to those with resolved CAE and controls, with the largest reduction in the frontal region, where epileptic spike activity was also most prominent. Spindle rate was inversely correlated with epileptic spike rate, consistent with a shared thalamocortical circuitry. The association between ASM exposure and spindle rate varied by disease state. In active CAE, treated children had higher spindle rates than untreated children. In resolved CAE, treated children had lower spindle rates than untreated children. In contrast, ASM-related differences in spike rate were more limited, although a medication-specific reduction was observed in active CAE. These findings support sleep spindle rate as a candidate physiological marker of disease state and ASM exposure in children with CAE. Given the established role of sleep spindles in memory consolidation, these findings support testing of whether spindle disruption provides a mechanistic link between epileptic thalamocortical activity and cognitive vulnerability in CAE.

neuroscience↗

bioRxiv2025

CaNetiCs - An Open-Source Toolbox for Standardized Dimensionality Reduction of Neuronal Calcium Activity

The widespread use of calcium imaging has produced large-scale datasets capturing neuronal population activity across diverse experimental contexts, posing challenges for analyzing complex, high-dimensional data. Dimensionality reduction (DR) methods have been pivotal in addressing these challenges by simplifying data into interpretable, low-dimensional structures, while capturing essential network dynamics. Among DR methods, Nonnegative Matrix Factorization (NMF) can produce biologically meaningful representations through its nonnegativity constraint and parts-based decomposition, making it especially suited for analyzing neuronal calcium signals. To enhance accessibility and standardization in the analysis of state-dependent neuronal dynamics, we introduce Calcium Network dynamiCs (CaNetiCs), an open-source toolbox centered on NMF, integrating standardized DR methods (PCA, ICA, UMAP), geometric low-dimensional component space analyses, and neuronal network simulation modules. We validate our toolbox by applying it to two diverse experimental datasets that describe responses to graded anesthesia: whole-ganglion cellular calcium imaging of C. elegans and two-photon imaging of murine somatosensory cortex. Our analyses recapitulate previously observed trends, such as network suppression and decorrelation with anesthesia, while uncovering novel insights into neuronal activity under differing contexts. CaNetiCs provides an accessible, modular, and interpretable framework, facilitating broader adoption of standardized dimensionality reduction methodologies for deeper exploration of neuronal network dynamics across experimental paradigms. The open-source code, along with documentation, is available at https://github.com/dannycarbonero/CaNetiCs.

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bioRxiv2025

Changes in slow oscillations and sleep spindles by auditory stimulation positively correlate with memory consolidation in children with epilepsy and controls

BackgroundSleep-dependent memory consolidation is supported by sleep spindles during stages 2 and 3 non-rapid eye movement sleep. Sleep spindles and sleep-dependent memory consolidation are both decreased in Rolandic epilepsy (RE). Non-invasive auditory stimulation evokes SOs and SO-spindle complexes in healthy adults but the impact on memory consolidation has been inconsistent. ObjectiveWe investigated the effects of auditory stimulation during sleep on SOs, SO-spindle complexes, and sleep-dependent memory consolidation in children with RE and controls. MethodsA prospective cross-over study was conducted in children with RE and control. Children completed two nap visits with auditory or sham stimulation. SOs and SO-spindle complexes rates were measured offline using validated detectors. Sleep-dependent memory consolidation was assessed using the motor sequence typing task. ResultsAuditory stimulation evoked SOs and SO-spindle complexes broadly with maximal effect over frontal electrodes. Compared to sham, stimulation delivered during background activity evoked SOs (29.8% increase, p<0.001) and SO-spindle complexes (16.8% increase, p<0.001) and stimulations delivered near the peak of an ongoing SO upstate maximally evoked SOs (51.3% increase, p<0.001) and SO-spindle complexes (32.3% increase, p<0.001). Changes in frontal SO (1.9% improvement per increase in SO/min; p<0.001) and SO-spindle complexes (9.5% improvement per increase in SO-spindle/min) event rates due to auditory stimulation positively predicted changes in sleep-dependent memory consolidation. ConclusionAuditory stimulation reliably modulates sleep oscillations when delivered on background activity and during the upstate of SOs. As increased event rates improve memory consolidation, stimulation paradigms to increase SO and SO-spindle complex rates are required to enhance memory.

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bioRxiv2025

A hierarchical cascade of sleep rhythms drive memory consolidation in humans and are disrupted in epilepsy.

The cross-regional interplay of slow oscillations, sleep spindles, and ripples during sleep is believed to support systems memory consolidation but is understudied in humans. Using a validated behavioral task and intracranial neural recordings from orbitofrontal cortex, thalamus, and hippocampus in 19 epilepsy patients, we examined the cross-regional interplay of sleep-oscillations and their role in memory consolidation. Orbitofrontal slow oscillations robustly modulate sleep rhythms both within and across regions. Most combinations of oscillation rates predict overnight memory consolidation, but hippocampal ripple rate and coupled hippocampal-orbitofrontal ripples were the strongest positive predictors of memory consolidation. In contrast, epileptic spikes coupled to sleep oscillations strongly predicted reduced memory consolidation, with the strongest negative effect observed when epileptic spikes were coupled to slow oscillations. These findings provide direct evidence of the hierarchical cascade of sleep oscillations in human memory processing and reveal how epileptic spikes disrupt this process in patients with epilepsy.

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bioRxiv2024

Impaired sleep-dependent memory consolidation predicted by reduced sleep spindles in Rolandic epilepsy

Background and ObjectivesSleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy. MethodsIn this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector. ResultsTwenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute). DiscussionChildren with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

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bioRxiv2024

Photothrombosis induced cortical stroke produces electrographic epileptic biomarkers in mice

ObjectiveInterictal epileptiform spikes, high-frequency ripple oscillations, and their co-occurrence (spike ripples) in human scalp or intracranial voltage recordings are well-established epileptic biomarkers. While clinically significant, the neural mechanisms generating these electrographic biomarkers remain unclear. To reduce this knowledge gap, we introduce a novel photothrombotic stroke model in mice that reproduces focal interictal electrographic biomarkers observed in human epilepsy. MethodsWe induced a stroke in the motor cortex of C57BL/6 mice unilaterally (N=7) using a photothrombotic procedure previously established in rats. We then implanted intracranial electrodes (2 ipsilateral and 2 contralateral) and obtained intermittent local field potential (LFP) recordings over several weeks in awake, behaving mice. We evaluated the LFP for focal slowing and epileptic biomarkers - spikes, ripples, and spike ripples - using both automated and semi-automated procedures. ResultsDelta power (1-4 Hz) was higher in the stroke hemisphere than the non-stroke hemisphere in all mice (p<0.001). Automated detection procedures indicated that compared to the non-stroke hemisphere, the stroke hemisphere had an increased spike ripple (p=0.006) and spike rates (p=0.039), but no change in ripple rate (p=0.98). Expert validation confirmed the observation of elevated spike ripple rates (p=0.008) and a trend of elevated spike rate (p=0.055) in the stroke hemisphere. Interestingly, the validated ripple rate in the stroke hemisphere was higher than the non-stroke hemisphere (p=0.031), highlighting the difficulty of automatically detecting ripples. Finally, using optimal performance thresholds, automatically detected spike ripples classified the stroke hemisphere with the best accuracy (sensitivity 0.94, specificity 0.94). SignificanceCortical photothrombosis-induced stroke in commonly used C57BL/6 mice produces electrographic biomarkers as observed in human epilepsy. This model represents a new translational cortical epilepsy model with a defined irritative zone, which can be broadly applied in transgenic mice for cell type specific analysis of the cellular and circuit mechanisms of pathologic interictal activity. Key PointsO_LICortical photothrombosis in mice produces stroke with characteristic intermittent focal delta slowing. C_LIO_LICortical photothrombosis stroke in mice produces the epileptic biomarkers spikes, ripples, and spike ripples. C_LIO_LIAll biomarkers share morphological features with the corresponding human correlate. C_LIO_LISpike ripples better lateralize to the lesional cortex than spikes or ripples. C_LIO_LIThis cortical model can be applied in transgenic mice for mechanistic studies. C_LI

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bioRxiv2023

Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings

Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.

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bioRxiv2023

Preliminary evidence of a relationship between sleep spindles and treatment response in epileptic encephalopathy

ObjectiveEpileptic encephalopathy with spike wave activation in sleep (EE-SWAS) is a challenging neurodevelopmental disease characterized by abundant epileptiform spikes during non-rapid eye movement (NREM) sleep accompanied by cognitive dysfunction. The mechanism of cognitive dysfunction is unknown, but treatment with high-dose diazepam may improve symptoms. Spike rate does not predict treatment response, but spikes may disrupt sleep spindles. We hypothesized that in patients with EE-SWAS: 1) spikes and spindles would be anticorrelated, 2) high-dose diazepam would increase spindles and decrease spikes, and 3) spindle response would be greater in those with cognitive improvement. MethodsConsecutive EE-SWAS patients treated with high-dose diazepam that met criteria were included. Using a validated automated spindle detector, spindle rate, duration, and percentage were computed in pre- and post-treatment NREM sleep. Spikes were quantified using a validated automated spike detector. Cognitive response was determined from chart review. ResultsSpindle rate was anticorrelated with spike rate in the channel with the maximal spike rate (p=0.002) and averaged across all channels (p=0.0005). Spindle rate, duration, and percentage each increased, and spike rate decreased, after high-dose diazepam treatment (p[≤]2e-5, all tests). Spindle rate, duration, and percentage (p[≤]0.004, all tests) were increased in patients with cognitive improvement after treatment, but not those without. Changes in spike rate did not distinguish between groups. InterpretationThese findings confirm thalamocortical disruption in EE-SWAS, identify a mechanism through which benzodiazepines may support cognitive recovery, and introduce sleep spindles as a promising mechanistic biomarker to detect treatment response in severe epileptic encephalopathies.

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bioRxiv2023

Human thalamic recordings reveal that epileptic spikes block sleep spindle production during non-rapid eye movement sleep

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Several epileptic encephalopathies share the trait of spike-wave activation during non-rapid eye movement sleep (EE-SWAS), a state dominated by sleep oscillations known to coordinate offline memory consolidation. How epileptic activity impacts these thalamocortical sleep oscillations has not been directly observed in humans. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we reconcile prior conflicting observations about how epileptic spikes coordinate with sleep oscillations and provide direct evidence for epileptic spike interference of sleep spindle production. We find that slow oscillations facilitate both epileptic spikes and sleep spindles during stage 2 sleep (N2) at different phases of the slow oscillation. We show that sleep activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate is increased after a cortical spike, p[~]0). Thalamic spikes increase the spindle refractory period (p<1.5e-21). In patients with EE-SWAS, the abundance of thalamic spikes result in downregulation of spindles for 30 seconds after each thalamic spike (p=3.4e-11) and decreased overall spindle rate across N2 (p=2e-7). These direct human thalamocortical observations identify a novel mechanism through which epileptiform spikes could impact cognitive function, wherein sleep-activated epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy.

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bioRxiv2023

A parsimonious model of the universal 1/f-like behavior of neural field spectra

Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo, without requiring a specific biological mechanism or collective critical behavior.

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bioRxiv2023

Different methods to estimate the phase of neural rhythms agree, but only during times of low uncertainty

Rhythms are a common feature of brain activity. Across different types of rhythms, the phase has been proposed to have functional consequences, thus requiring its accurate specification from noisy data. Phase is conventionally specified using techniques that presume a frequency band-limited rhythm. However, in practice, observed brain rhythms are typically non-sinusoidal and amplitude modulated. How these features impact methods to estimate phase remains unclear. To address this, we consider three phase estimation methods, each with different underlying assumptions about the rhythm. We apply these methods to rhythms simulated with different generative mechanisms and demonstrate inconsistency in phase estimates across the different methods. We propose two improvements to the practice of phase estimation: (1) estimating confidence in the phase estimate, and (2) examining the consistency of phase estimates between two (or more) methods. Significant StatementRhythms in the brain can coordinate the activity of individual neurons and communication within brain networks, making these rhythms a target for therapeutic interventions. Brain rhythms manifest in diverse ways, appearing with sinusoidal and non-sinusoidal waveforms, multiple peak frequencies, and variable durations. Across this diversity of rhythms, an important feature to characterize is the rhythms phase. In this manuscript, we demonstrate the ambiguity inherent in estimating phase from neural data. We propose estimating uncertainty in the phase and comparing multiple phase estimators improves phase estimation in practice.

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bioRxiv2022

The Features Underlying the Memorability of Objects

What makes certain images more memorable than others? While much of memory research has focused on participant effects, recent studies employing a stimulus-centric perspective have sparked debate on the determinants of memory, including the roles of semantic and visual features and whether the most prototypical or atypical items are best remembered. Prior studies have typically relied on constrained stimulus sets, limiting a generalized view of the features underlying what we remember. Here, we collected 1+ million memory ratings for a naturalistic dataset of 26,107 object images designed to comprehensively sample concrete objects. We establish a model of object features that is predictive of image memorability and examined whether memorability could be accounted for by the typicality of the objects. We find that semantic features exert a stronger influence than perceptual features on what we remember and that the relationship between memorability and typicality is more complex than a simple positive or negative association alone. TEASERSemantic versus perceptual features more heavily influence what we remember, and memorability cannot be reduced to typicality.

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bioRxiv2022

Normative sleep spindle database and findings from 772 healthy children from birth through 18 years

Work in the last two decades has identified sleep spindles, discrete "sigma band" oscillations during stage 2 sleep, as a key oscillatory mechanism required for off-line memory consolidation. Although, sleep spindles are known to evolve concomitant with brain maturation and reflect cognitive function across the lifespan, the details of this developmental trajectory are unknown. To address this, we curated a database of sleep electroencephalograms from 772 developmentally normal children to characterize spindles from birth through 18 years. After validating an automated spindle detector against ~20,000 hand-marked spindles across ages, we demonstrate that sleep spindle features follow distinct age-specific patterns in distribution, rate, duration, frequency, estimated refractory period, and inter-hemispheric spindle lag. These data expand our current knowledge of normal physiological brain development and provide a large normative database to detect deviations in sleep spindles to aid discovery, biomarker development, and diagnosis in pediatric neurodevelopmental disorders.

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bioRxiv2021

A State Space Modeling Approach to Real-Time Phase Estimation

Brain rhythms have been proposed to facilitate brain function, with an especially important role attributed to the phase of low frequency rhythms. Understanding the role of phase in neural function requires interventions that perturb neural activity at a target phase, necessitating estimation of phase in real-time. Current methods for real-time phase estimation rely on bandpass filtering, which assumes narrowband signals and couples the signal and noise in the phase estimate, adding noise to the phase and impairing detections of relationships between phase and behavior. To address this, we propose a state space phase estimator for real-time tracking of phase. By tracking the analytic signal as a latent state, this framework avoids the requirement of bandpass filtering, separately models the signal and the noise, accounts for rhythmic confounds, and provides credible intervals for the phase estimate. We demonstrate in simulations that the state space phase estimator outperforms current state-of-the-art real-time methods in the contexts of common confounds such as broadband rhythms, phase resets and co-occurring rhythms. Finally, we show applications of this approach to in vivo data. The method is available as a ready-to-use plug-in for the OpenEphys acquisition system, making it widely available for use in experiments.

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bioRxiv2019

A statistical modeling framework to assess cross-frequency coupling while accounting for confounding effects

Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate how CFC evolves during seizures and is affected by electrical stimuli.

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