Even though human experience unfolds continuously in time, it is not strictly linear; instead, it entails cascading processes building hierarchical cognitive structures. For instance, during speech perception, humans transform a continuously varying acoustic signal into phonemes, words, and meaning, and these levels all have different, interdependent temporal structures. Deconvolution analysis has recently emerged as a promising tool for disentangling electrophysiological brain responses related to such complex models of perception. Here we introduce the Eelbrain Python toolkit, which makes this kind of analysis easy and accessible. We demonstrate its use, using continuous speech as a sample paradigm, with a freely available EEG dataset of audiobook listening. A companion GitHub repository provides the complete source code for the analysis, from raw data to group level statistics. More generally, we advocate a hypothesis-driven approach in which the experimenter specifies a hierarchy of time-continuous representations that are hypothesized to have contributed to brain responses, and uses those as predictor variables for the electrophysiological signal. This is analogous to a multiple regression problem, but with the addition of the time dimension. The deconvolution analysis decomposes the brain signal into distinct responses associated with the different predictor variables by estimating a multivariate temporal response function (mTRF), quantifying the influence of each predictor on brain responses as a function of time(-lags). This allows asking two questions about the predictor variables: 1) Is there a significant neural representation corresponding to this predictor variable? And if so, 2) what are the temporal characteristics of the neural response associated with it? Thus, different predictor variables can be systematically combined and evaluated to jointly model neural processing at multiple hierarchical levels. We discuss applications of this approach, including the potential for linking algorithmic/representational theories at different cognitive levels to brain responses through appropriate linking models.

No abstract

Spectral analysis using overlapping sliding windows is among the most widely used techniques in analyzing nonstationary time series. Although sliding window analysis is convenient to implement, the resulting estimates are sensitive to the window length and overlap size. In addition, it undermines the dynamics of the time series as the estimate associated to each window uses only the data within. Finally, the overlap between consecutive windows hinders a precise statistical assessment. In this paper, we address these shortcomings by explicitly modeling the spectral dynamics through integrating the multitaper method with state-space models in a Bayesian estimation framework. The underlying states pertaining to the eigen-spectral quantities arising in multitaper analysis are estimated using instances of the Expectation-Maximization algorithm, and are used to construct spectrograms and their respective confidence intervals. We propose two spectral estimators that are robust to noise and are able to capture spectral dynamics at high spectrotemporal resolution. We provide theoretical analysis of the bias-variance trade-off, which establishes performance gains over the standard overlapping multitaper method. We apply our algorithms to synthetic data as well as real data from human EEG and electric network frequency recordings, the results of which validate our theoretical analysis.

ImportanceOpioids administered to treat postsurgical pain are a major contributor to the opioid crisis, leading to chronic use in a considerable proportion of patients. Initiatives promoting opioid-free or opioid-sparing modalities of perioperative pain management have led to reduced opioid administration in the operating room, but this reduction could have unforeseen detrimental effects in terms of postoperative pain outcomes, as the relationship between intraoperative opioid usage and later opioid requirements is not well understood.ObjectiveTo characterize the association between intraoperative opioid usage and postoperative pain and opioid requirements.Design, Setting, and ParticipantsThis retrospective cohort study evaluated electronic health record data from a quaternary care academic medical center (Massachusetts General Hospital) for adult patients who underwent noncardiac surgery with general anesthesia from April 2016 to March 2020. Patients who underwent cesarean surgery, received regional anesthesia, received opioids other than fentanyl or hydromorphone, were admitted to the intensive care unit, or who died intraoperatively were excluded. Statistical models were fitted on the propensity weighted data set to characterize the effect of intraoperative opioid exposures on primary and secondary outcomes. Data were analyzed from December 2021 to October 2022.ExposuresIntraoperative fentanyl and intraoperative hydromorphone average effect site concentration estimated using pharmacokinetic/pharmacodynamic models.Main Outcomes and MeasuresThe primary study outcomes were the maximal pain score during the postanesthesia care unit (PACU) stay and the cumulative opioid dose, quantified in morphine milligram equivalents (MME), administered during the PACU stay. Medium- and long-term outcomes associated with pain and opioid dependence were also evaluated.ResultsThe study cohort included a total of 61 249 individuals undergoing surgery (mean [SD] age, 55.44 [17.08] years; 32 778 [53.5%] female). Increased intraoperative fentanyl and intraoperative hydromorphone were both associated with reduced maximum pain scores in the PACU. Both exposures were also associated with a reduced probability and reduced total dosage of opioid administration in the PACU. In particular, increased fentanyl administration was associated with lower frequency of uncontrolled pain; a decrease in new chronic pain diagnoses reported at 3 months; fewer opioid prescriptions at 30, 90, and 180 days; and decreased new persistent opioid use, without significant increases in adverse effects.Conclusions and RelevanceContrary to prevailing trends, reduced opioid administration during surgery may have the unintended outcome of increasing postoperative pain and opioid consumption. Conversely, improvements in long-term outcomes might be achieved by optimizing opioid administration during surgery.