Andrew Perley

All Publications

• A mutual information measure of phase-amplitude coupling using gamma generalized linear models. Frontiers in computational neuroscience Perley, A. S., Coleman, T. P. 2024; 18: 1392655

  Abstract

  Introduction: Cross frequency coupling (CFC) between electrophysiological signals in the brain is a long-studied phenomenon and its abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling, specifically phase-amplitude coupling (PAC), do not attempt to capture the phase and amplitude statistical relationships.Methods: In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using a gamma distributed generalized linear model (GLM) with a Fourier basis of regressors. We perform model selection with minimum description length (MDL) principle, demonstrate a method for assessing goodness-of-fit (GOF), and showcase the efficacy of this approach in multiple electroencephalography (EEG) datasets. Secondly, we showcase how we can utilize the mutual information, which operates on the joint distribution, as a canonical measure of coupling, as it is non-zero and non-negative if and only if the phase and amplitude are not statistically independent. In addition, we build off of previous work by Martinez-Cancino et al., and Voytek et al., and show that the information density, evaluated using our method along the given sample path, is a promising measure of time-resolved PAC.Results: Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase-amplitude coupling through receiver operating characteristic (ROC) curve analysis. To validate our method, we test on invasive EEG recordings by generating comodulograms, and compare our method to the gold standard PAC measure, Modulation Index, demonstrating comparable performance in exploratory analysis. Furthermore, to showcase its use in joint gut-brain electrophysiology data, we generate topoplots of simultaneous high-density EEG and electrgastrography recordings and reproduce seminal work by Richter et al. that demonstrated the existence of gut-brain PAC. Using simulated data, we validate our method for different types of time-varying coupling and then demonstrate its performance to track time-varying PAC in sleep spindle EEG and mismatch negativity (MMN) datasets.Conclusions: Our new measure of PAC using Gamma GLMs and mutual information demonstrates a promising new way to compute PAC values using the full joint distribution on amplitude and phase. Our measure outperforms the most common existing measures of PAC, and show promising results in identifying time varying PAC in electrophysiological datasets. In addition, we provide for using our method with multiple comparisons and show that our measure potentially has more statistical power in electrophysiologic recordings using simultaneous gut-brain datasets.

  View details for DOI 10.3389/fncom.2024.1392655

  View details for PubMedID 38841426

• Miniaturized wireless gastric pacing via inductive power transfer with non-invasive monitoring using cutaneous Electrogastrography. Bioelectronic medicine Perley, A., Roustaei, M., Aguilar-Rivera, M., Kunkel, D. C., Hsiai, T. K., Coleman, T. P., Abiri, P. 2021; 7 (1): 12

  Abstract

  BACKGROUND: Gastroparesis is a debilitating disease that is often refractory to pharmacotherapy. While gastric electrical stimulation has been studied as a potential treatment, current devices are limited by surgical complications and an incomplete understanding of the mechanism by which electrical stimulation affects physiology.METHODS: A leadless inductively-powered pacemaker was implanted on the gastric serosa in an anesthetized pig. Wireless pacing was performed at transmitter-to-receiver distances up to 20mm, frequency of 0.05Hz, and pulse width of 400ms. Electrogastrogram (EGG) recordings using cutaneous and serosal electrode arrays were analyzed to compute spectral and spatial statistical parameters associated with the slow wave.RESULTS: Our data demonstrated evident change in EGG signal patterns upon initiation of pacing. A buffer period was noted before a pattern of entrainment appeared with consistent and low variability in slow wave direction. A spectral power increase in the EGG frequency band during entrainment also suggested that pacing increased strength of the slow wave.CONCLUSION: Our preliminary in vivo study using wireless pacing and concurrent EGG recording established the foundations for a minimally invasive approach to understand and optimize the effect of pacing on gastric motor activity as a means to treat conditions of gastric dysmotility.

  View details for DOI 10.1186/s42234-021-00074-8

  View details for PubMedID 34425917

• Spontaneous enteric nervous system activity generates contractile patterns prior to maturation of gastrointestinal motility. Neurogastroenterology and motility Dershowitz, L. B., Bueno Garcia, H., Perley, A. S., Coleman, T. P., Kaltschmidt, J. A. 2024: e14890

  Abstract

  Spontaneous neuronal network activity is essential to the functional maturation of central and peripheral circuits, yet whether this is a feature of enteric nervous system development has yet to be established. Although enteric neurons are known exhibit electrophysiological properties early in embryonic development, no connection has been drawn between this neuronal activity and the development of gastrointestinal (GI) motility patterns.We use ex vivo GI motility assays with newly developed unbiased computational analyses to identify GI motility patterns across mouse embryonic development.We find a previously unknown pattern of neurogenic contractions termed "clustered ripples" that arises spontaneously at embryonic day 16.5, an age earlier than any identified mature GI motility patterns. We further show that these contractions are driven by nicotinic cholinergic signaling.Clustered ripples are neurogenic contractile activity that arise from spontaneous ENS activity and precede all known forms of neurogenic GI motility. This earliest motility pattern requires nicotinic cholinergic signaling, which may inform pharmacology for enhancing GI motility in preterm infants.

  View details for DOI 10.1111/nmo.14890

  View details for PubMedID 39118231

• Spontaneous enteric nervous system activity precedes maturation of gastrointestinal motility. bioRxiv : the preprint server for biology Dershowitz, L. B., Garcia, H. B., Perley, A. S., Coleman, T. P., Kaltschmidt, J. A. 2023

  Abstract

  Spontaneous neuronal network activity is essential in development of central and peripheral circuits, yet whether this is a feature of enteric nervous system development has yet to be established. Using ex vivo gastrointestinal (GI) motility assays with unbiased computational analyses, we identify a previously unknown pattern of spontaneous neurogenic GI motility. We further show that this motility is driven by cholinergic signaling, which may inform GI pharmacology for preterm patients.

  View details for DOI 10.1101/2023.08.03.551847

  View details for PubMedID 37577464

  View details for PubMedCentralID PMC10418201

• A Convex Formulation of Point Process Heartbeat Dynamics using a Gamma Generalized Linear Model Perley, A., Subramanian, S., Coleman, T. P., IEEE IEEE. 2023

  View details for DOI 10.1109/BSN58485.2023.10331291

  View details for Web of Science ID 001116732100062

• A Mutual Information Measure of Phase-Amplitude Coupling using High Dimensional Sparse Models. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Perley, A., Coleman, T. P. 2022; 2022: 21-24

  Abstract

  Cross frequency coupling (CFC) between electrophysiological signals in the brain has been observed and it's abnormalities have been observed in conditions such as Parkinson's disease and epilepsy. More recently, CFC has been observed in stomach-brain electrophysiologic studies and thus becomes an enticing possible target for diseases involving aberrations of the gut-brain axis. However, current methods of detecting coupling do not attempt to capture the underlying statistical relationships that give rise to this coupling. In this paper, we demonstrate a new method of calculating phase amplitude coupling by estimating the mutual information between phase and amplitude, using a flexible parametric modeling approach. Specifically, we develop an exponential generalized linear model (GLM) to model amplitude given phase, using a high dimensional basis of von-Mises function regressors and l1 regularized model selection. Using synthetically generated gut-brain coupled signals, we demonstrate that our method outperforms the existing gold-standard methods for detectable low-levels of phase amplitude coupling through receiver operating characteristic (ROC) curve analysis.

  View details for DOI 10.1109/EMBC48229.2022.9871816

  View details for PubMedID 36086427