Biology subjects

Pekar, J. J.

Publications and source records attributed to Pekar, J. J..

4 recordsLinked to original sources

bioRxiv2021

Methodological considerations in analyzing synchronization of resting-state brain networks with the intrinsic electrical rhythm of the stomach: Advantages of weighted phase-locking

PurposeTo evaluate the amplitude-weighted phase-locking value (awPLV) as a measure of synchronization of brain resting-state networks (RSNs) with the gastric basal electrical rhythm (BER). MethodsA recent study combined rsfMRI with concurrent cutaneous electrogastrography (EGG), in a highly-sampled individual who underwent 22 scanning sessions (two 15-minute runs per session) at 3.0 Tesla. After excluding three sessions due to weak EGG signals, 9.5 hours of data remained, from which 18 RSNs were estimated using spatial independent component analysis. Previously, using the phase-locking value (PLV), three of the 18 RSNs were determined to be synchronized with the BER. However, RSN power fluctuations in the gastric frequency band could reduce sensitivity of PLV. Accordingly, the current reanalysis used awPLV to unweight contributions from low power epochs. Mismatched EGG and rsfMRI data (from different days) served as surrogate data; for each RSN, empirical awPLV was compared with chance-level awPLV using a Wilcoxon rank test. P-values were adjusted using with a false discovery rate of 0.05. Additionally, simulations were performed to compare PLV and awPLV error rates under settings with a known ground truth. ResultsSimulations show high false-negative rates when using PLV, but not awPLV. Reanalysis of the highly-sampled individual data using awPLV indicates that 11 of the 18 RSNs were synchronized with the BER. ConclusionSimulations indicate that awPLV is a more sensitive measure of stomach/brain synchronization than PLV. Reanalysis results imply communication between the enteric nervous system and brain circuits not typically considered responsive to gastric state or function.

neuroscience↗

bioRxiv2020

Differences in functional connectivity distribution after transcranial direct-current stimulation: a connectivity density point of view

In this manuscript we consider the problem of relating functional connectivity measurements viewed as statistical distributions to outcomes. We demonstrate the utility of using the distribution of connectivity on a study of resting state functional magnetic resonance imaging association with an intervention. Specifically, we consider 47 primary progressive aphasia (PPA) patients with various levels of language abilities. These patients were randomly assigned to two treatment arms, tDCS (transcranial direct-current stimulation and language therapy) vs sham (language therapy only), in a clinical trial. We propose a novel approach to analyze the effect of direct stimulation on functional connectivity. We estimate the density of correlations among the regions of interest (ROIs) and study the difference in the density post-intervention between treatment arms. We discover that it is the tail of the density, rather than the mean or lower order moments of the distribution, that demonstrates a significant impact in the classification. This approach has several benefits. Among them, it drastically reduces the number of multiple comparisons compared to edge-wise analysis. In addition, it allows for the investigation of the impact of functional connectivity on the outcomes where the connectivity is not geometrically localized.

neuroscience↗

bioRxiv2020

Phase-locking of resting-state brain networks with the gastric basal electrical rhythm

A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca. 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated using dual regression; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.

neuroscience↗

bioRxiv2019

Improved state change estimation in dynamic functionalconnectivity using hidden semi-Markov models

The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subjects most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.

neuroscience↗