Efficient spline regression for neural spiking data

Characterisation of nocturnal arrhythmia avalanche dynamics: Insights from generalised linear model analysis

Nocturnal arrhythmia avalanche (NAA) episodes, characterised by transient non-sustained cardiac arrhythmias during sleep, have been demonstrated as a predictor of adverse cardiovascular events. However, their dynamics and association with sleep architecture and events remain unclear. While generalised linear models (GLM) have captured sleep-disordered breathing (SDB) dynamics, their application to NAA remains underexplored. This study explored whether changes in sleep architecture contribute to nocturnal arrhythmias and if the impact of sleep stages, SDB, and arousal events on these arrhythmias varies by demographic factors. We analysed 7341 ECG recordings from the multi-ethnic study of atherosclerosis (MESA) and the sleep heart health study (SHHS) datasets. R-R intervals were divided into 10-min periods to detect NAA, defined as a 30% drop from baseline followed by recovery to 90% of baseline. A GLM framework was developed to characterise NAA episodes as functions of SDB, sleep arousal events, sleep stages, and prior NAA episodes. The GLM analysis revealed that NAA occurrence was 18% and 30% higher during non-rapid eye movement (NREM) light sleep compared with deep sleep in SHHS (p < 0.001) and MESA (p < 0.001), respectively. SDB events increased the NAA risk in 34% of participants, and arousals in 29%. In SHHS, the impact of SDB on NAA was 5% greater in men (p = 0.018), while the arousal effects were more pronounced in those over 75, highlighting the role of demographic factors in modulating arrhythmia risk. These findings demonstrate the utility of the GLM framework in modelling the dynamics of nocturnal arrhythmias and their associations with sleep disruptions and architecture.

Schizophrenia pathology reverse-translated into mouse shows hippocampal hyperactivity, psychosis behaviors and hyper-synchronous events

Decades of research into the function of the medial temporal lobe has driven curiosity around clinical outcomes associated with hippocampal dysfunction, including psychosis. Post-mortem analyses of brain tissue from human schizophrenia brain show decreased expression of the NMDAR subunit GluN1 confined to the dentate gyrus with evidence of downstream hippocampal hyperactivity in CA3 and CA1. Little is known about the mechanisms of the emergence of hippocampal hyperactivity as a putative psychosis biomarker. We have developed a reverse-translation mouse to study critical neural features. We had previously studied a dentate gyrus (DG)-specific GluN1 KO, which displays hippocampal hyperactivity and a psychosis-relevant behavioral phenotype. Here, we expressed an inhibitory DREADD (pAAV-CaMKIIa-hM4D(Gi)-mCherry) in granule cells of the mouse dentate gyrus, and continuously inhibited the region for 21 days in adolescent (6 weeks) and adult (10 weeks) C57BL/6 J mice with DREADD agonist Compound 21 (C21). Following this period, we quantified activity in the hippocampal subfields by assessing cFos expression, hippocampally mediated behaviors, and hippocampal local field potential with an intracerebral probe with continual monitoring over time. DG inhibition during adolescence generates an increase in hippocampal activity in CA3 and CA1, impairs social cognition and spatial working memory, as well as shows evidence of increased activity in local field potentials as spontaneous synchronous bursts of activity, which we term hyper-synchronous events (HSEs) in hippocampus. The same DG inhibition delivered during adulthood in the mouse lacks these outcomes. These results suggest a sensitive period in development in which the hippocampus is susceptible to DG inhibition resulting in hippocampal hyperactivity and psychosis-like behavioral outcomes.

Detecting rhythmic spiking through the power spectra of point process model residuals

Objective. Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ('RP', the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established 'shuffling' procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.Approach. In a novel 'residuals' method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the precedingnrmilliseconds. Finally, we compute the PSD of the model's residuals.Main results. We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey-in which alpha-beta oscillations (8-30 Hz) were anticipated-the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.Significance. These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Detecting rhythmic spiking through the power spectra of point process model residuals

Objective

Oscillations figure prominently as neurological disease hallmarks and neuromodulation targets. To detect oscillations in a neuron's spiking, one might attempt to seek peaks in the spike train's power spectral density (PSD) which exceed a flat baseline. Yet for a non-oscillating neuron, the PSD is not flat: The recovery period ("RP", the post-spike drop in spike probability, starting with the refractory period) introduces global spectral distortion. An established "shuffling" procedure corrects for RP distortion by removing the spectral component explained by the inter-spike interval (ISI) distribution. However, this procedure sacrifices oscillation-related information present in the ISIs, and therefore in the PSD. We asked whether point process models (PPMs) might achieve more selective RP distortion removal, thereby enabling improved oscillation detection.

Approach

In a novel "residuals" method, we first estimate the RP duration (nr) from the ISI distribution. We then fit the spike train with a PPM that predicts spike likelihood based on the time elapsed since the most recent of any spikes falling within the preceding nr milliseconds. Finally, we compute the PSD of the model's residuals.

Main results

We compared the residuals and shuffling methods' ability to enable accurate oscillation detection with flat baseline-assuming tests. Over synthetic data, the residuals method generally outperformed the shuffling method in classification of true- versus false-positive oscillatory power, principally due to enhanced sensitivity in sparse spike trains. In single-unit data from the internal globus pallidus (GPi) and ventrolateral anterior thalamus (VLa) of a parkinsonian monkey -- in which alpha-beta oscillations (8-30 Hz) were anticipated -- the residuals method reported the greatest incidence of significant alpha-beta power, with low firing rates predicting residuals-selective oscillation detection.

Significance

These results encourage continued development of the residuals approach, to support more accurate oscillation detection. Improved identification of oscillations could promote improved disease models and therapeutic technologies.

Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing

Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.

Integrating Statistical and Machine Learning Approaches for Neural Classification

Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as neural recording technologies have advanced to produce simultaneous spiking data from massive populations, classical statistical methods often lack the computational efficiency required to handle such data. Machine learning (ML) approaches are known for enabling efficient large scale data analyses; however, they typically require massive training sets with balanced data, along with accurate labels to fit well. Additionally, model assessment and interpretation are often more challenging for ML than for classical statistical methods. To address these challenges, we develop an integrated framework, combining statistical modeling and machine learning approaches to identify the coding properties of neurons from large populations. In order to demonstrate this framework, we apply these methods to data from a population of neurons recorded from rat hippocampus to characterize the distribution of spatial receptive fields in this region.

Dynamic models of obstructive sleep apnea provide robust prediction of respiratory event timing and a statistical framework for phenotype exploration

Obstructive sleep apnea (OSA), in which breathing is reduced or ceased during sleep, affects at least 10% of the population and is associated with numerous comorbidities. Current clinical diagnostic approaches characterize severity and treatment eligibility using the average respiratory event rate over total sleep time (apnea-hypopnea index). This approach, however, does not characterize the time-varying and dynamic properties of respiratory events that can change as a function of body position, sleep stage, and previous respiratory event activity. Here, we develop a statistical model framework based on point process theory that characterizes the relative influences of all these factors on the moment-to-moment rate of event occurrence. Our results provide new insights into the temporal dynamics of respiratory events, suggesting that most adults have a characteristic event pattern that involves a period of normal breathing followed by a period of increased probability of respiratory event occurrence, while significant differences in event patterns are observed among gender, age, and race/ethnicity groups. Statistical goodness-of-fit analysis suggests consistent and substantial improvements in our ability to capture the timing of individual respiratory events using our modeling framework. Overall, we demonstrate a more statistically robust approach to characterizing sleep disordered breathing that can also serve as a basis for identifying future patient-specific respiratory phenotypes, providing an improved pathway towards developing individualized treatments.