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Event-related potentials (ERPs) are a common approach for investigating the neural basis of cognition and disease. There exists a vast and growing literature of ERP-related articles, the scale of which motivates the need for efficient and systematic meta-analytic approaches for characterizing this research. Here we present an automated text-mining approach as a form of meta-analysis to examine the relationships between ERP terms, cognitive domains and clinical disorders. We curated dictionaries of terms, collected articles of interest, and measured co-occurrence probabilities in published articles between ERP components and cognitive and disorder terms. Collectively, this literature dataset allows for creating data-driven profiles for each ERP, examining key associations of each component, and comparing the similarity across components, ultimately allowing for characterizing patterns and associations between topics and components. Additionally, by examining large literature collections, novel analyses can be done, such as examining how ERPs of different latencies relate to different cognitive associations. This openly available dataset and project can be used both as a pedagogical tool, and as a method of inquiry into the previously hidden structure of the existing literature. This project also motivates the need for consistency in naming, and for developing a clear ontology of electrophysiological components.

A hallmark of electrophysiological brain activity is its 1/f-like spectrum – power decreases with increasing frequency. The steepness of this ‘roll-off’ is approximated by the spectral exponent, which in invasively recorded neural populations reflects the balance of excitatory to inhibitory neural activity (E:I balance). Here, we first establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals’ degree of this selective stimulus–brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

Abstract-Nolan and Temple Lang's Computing in the Statistics Curricula (2010) advocated for a shift in statistical education to broadly include computing. In the time since, individuals with training in both computing and statistics have become increasingly employable in the burgeoning data science field. In response, universities have developed new courses and programs to meet the growing demand for data science education. To address this demand, we created Data Science in Practice, a large-enrollment undergraduate course. Here, we present our goals for teaching this course, including: (1) conceptualizing data science as creative problem solving, with a focus on project-based learning, (2) prioritizing practical application, teaching and using standardized tools and best practices, and (3) scaling education through coursework that enables hands-on and classroom learning in a large-enrollment course. Throughout this course we also emphasize social context and data ethics to best prepare students for the interdisciplinary and impactful nature of their work. We highlight creative problem solving and strategies for teaching automation-resilient skills, while providing students the opportunity to create a unique data science project that demonstrates their technical and creative capacities.

The aperiodic (1/f-like) component of electrophysiological data, whereby power systematically decreases with increasing frequency, as quantified by the aperiodic exponent, has been shown to differentiate sleep stages. Previous studies typically measured this exponent over narrow frequency ranges and averaged across sleep stages. A systematic review following PRISMA 2020 guidelines, which identified 16 eligible studies examining aperiodic neural activity during sleep, revealed heterogeneous frequency ranges and methodological approaches across studies. Building on these insights, the present study expands the analysis to include wider frequency ranges and alternative models, such as detecting ‘knees’ in the aperiodic component, which reflect bends in the power spectrum indicating changes in the exponent. Additionally, we applied time-resolved analyses to examine the dynamic patterns of aperiodic activity during sleep. We analyzed data from two sources: intracranial EEG (iEEG) from 106 epilepsy patients and high-density EEG from 17 healthy individuals and compared different frequency ranges and model forms of aperiodic activity. Results showed that broadband aperiodic models and the inclusion of a ‘knee’ feature effectively captured sleep stage-dependent differences in aperiodic activity. The knee parameter exhibited stage-specific variations, indicating different processing timescales across sleep stages. Time-resolved analysis of the aperiodic exponent tracked sleep stage transitions and responses to external stimuli, highlighting rapidly varying temporal dynamics during sleep. These findings offer valuable insights into brain dynamics during sleep and reveal novel insights and interpretations for understanding aperiodic neural activity during sleep. Sleep involves dynamic changes in brain activity that unfold over time, reflected in the brain’s aperiodic EEG patterns. Incorporating the spectral ‘knee’—a bend in the EEG power spectrum—reveals stage-specific shifts in neural processing timescales, providing valuable insights into sleep dynamics.

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations have been linked to numerous physiological, cognitive, behavioral and disease states. Emerging evidence demonstrates that the aperiodic component has putative physiological interpretations and that it dynamically changes with age, task demands and cognitive states. Electrophysiological neural activity is typically analyzed using canonically defined frequency bands, without consideration of the aperiodic (1/f-like) component. We show that standard analytic approaches can conflate periodic parameters (center frequency, power, bandwidth) with aperiodic ones (offset, exponent), compromising physiological interpretations. To overcome these limitations, we introduce an algorithm to parameterize neural power spectra as a combination of an aperiodic component and putative periodic oscillatory peaks. This algorithm requires no a priori specification of frequency bands. We validate this algorithm on simulated data, and demonstrate how it can be used in applications ranging from analyzing age-related changes in working memory to large-scale data exploration and analysis.A method for parameterizing electrophysiological neural power spectra into periodic and aperiodic components is introduced, addressing limitations of common approaches. The method is validated in simulation and demonstrated on real data applications.

Understanding the functional organization of brain activity is a fundamental topic in neuroscience. Questions about how the brain coordinates information through space and time are often investigated with the use of neural field data–electrophysiological recordings of the aggregate electrical activity across groups of neurons. Such activity contains both periodic activity (neural oscillations), a common topic of investigation, and aperiodic activity, which has been less broadly studied, each of which have distinct interpretations. The overlap of these two components of activity is a source of difficulty for investigations which aim to measure and interpret the properties and dynamics of one or the other component, as methods that do not explicitly consider and measure both properties of the data are liable to conflate the two components. Despite this, many commonly employed analysis methods do not attempt to explicitly measure and separate both periodic aperiodic activity. In this work, we develop a new method for separating and measuring periodic and aperiodic activity, using frequency domain representations of neural field data. First, we propose a novel algorithm for parameterizing neural power spectra, and validate this approach on simulated data, and demonstrate how it can be applied to real datasets. Second, we systematically explore how power spectrum parameterization compares to canonical approaches, using the example of frequency band ratio measures. Here we show that such measures that analyze pre-defined frequency ranges without considering and separating aperiodic activity are liable to reflect confounded measures of aperiodic activity. Finally, we apply the novel method across a series of datasets, systematically exploring the properties and variability of periodic and aperiodic activity across the human cortex. In sum, this work motivates that both periodic and aperiodic activity are dynamic components, necessitating dedicated methods to appropriately measure and interpret changes in the data. In doing so, methods that do consider both aperiodic and periodic activity allow for better quantifications of brain activity that can be investigated for their putative relationships to demographics, cognition and disease states.

Place cells, first identified in the rat hippocampus as neurons that fire selectively at specific locations, are central to investigations of the neural underpinnings of spatial navigation. With recent work with human patients, identifying and characterizing place cells across species has become increasingly important for understanding the extent to which decades of rodent research generalize to humans and uncovering principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI), whereas human studies employ analysis of variance (ANOVA) - based approaches. These methodological differences may affect the identified place cell population, which complicates how their properties are interpreted and cross-species comparisons. To address this, we systematically applied multiple detection pipelines to human and rat datasets, supported by simulations that vary place-field properties. Our analyses and simulations demonstrate that spatial information and ANOVA-based approaches are responsive to distinct place field properties: spatial information primarily reflects the contrast between peak and average firing rates, while ANOVA emphasizes consistency across trials. Across species, rodent place cells revealed a broad spectrum of spatial tuning, including strongly tuned neurons with high spatial information (SI) and high ANOVA values. In contrast, human place cells lacked this strongly tuned population and exhibited a narrower distribution of tuning scores, concentrated at the lower end of both spatial tuning metrics. Despite these differences, both species had an overlapping population of neurons with weaker yet consistent spatial tuning, which may support important functional roles such as generalization and mixed selectivity. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation, while underscoring the functional importance of weaker-tuned neurons in cross-species comparisons.Place cells, first identified in the rat hippocampus as neurons that fire selectively at specific locations, are central to investigations of the neural underpinnings of spatial navigation. With recent work with human patients, identifying and characterizing place cells across species has become increasingly important for understanding the extent to which decades of rodent research generalize to humans and uncovering principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI), whereas human studies employ analysis of variance (ANOVA) - based approaches. These methodological differences may affect the identified place cell population, which complicates how their properties are interpreted and cross-species comparisons. To address this, we systematically applied multiple detection pipelines to human and rat datasets, supported by simulations that vary place-field properties. Our analyses and simulations demonstrate that spatial information and ANOVA-based approaches are responsive to distinct place field properties: spatial information primarily reflects the contrast between peak and average firing rates, while ANOVA emphasizes consistency across trials. Across species, rodent place cells revealed a broad spectrum of spatial tuning, including strongly tuned neurons with high spatial information (SI) and high ANOVA values. In contrast, human place cells lacked this strongly tuned population and exhibited a narrower distribution of tuning scores, concentrated at the lower end of both spatial tuning metrics. Despite these differences, both species had an overlapping population of neurons with weaker yet consistent spatial tuning, which may support important functional roles such as generalization and mixed selectivity. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation, while underscoring the functional importance of weaker-tuned neurons in cross-species comparisons.Place cells are neurons that become active in specific locations, and they play a critical role in how the brain supports navigation and memory. Place cells were first discovered in rats and later observed in humans, however, there has been a lack of direct comparisons between species using comparable approaches. Part of the difficulty doing so is that studies of rodent and human place cells have often relied on different analysis methods, making it difficult to determine if and how place-cell properties differ between species. To address this, in this study, we set out to understand how differences in place cell detection methods affect the identified place cell populations and interpretations of spatial coding across species.To do so, we compared the most prevalent detection methods used in rodent and human research side by side, applying them to datasets from both species and to simulations. We found that different methods emphasize different features of spatial responses, which changes which neurons are identified as place cells. Across species, rat recordings revealed a wide range of spatial responses, from neurons with sharply localized activity to those with broader but reliable patterns. Human recordings, by contrast, were more concentrated at weaker but consistent levels of tuning. Importantly, these weaker but consistent responses reflect an overlapping population of neurons found in both species, which may serve similar functional roles in supporting flexible spatial memory and generalization. By separating methodological effects from biological differences, we lay the groundwork for future cross-species studies for spatial coding.Author SummaryPlace cells are neurons that become active in specific locations, and they play a critical role in how the brain supports navigation and memory. Place cells were first discovered in rats and later observed in humans, however, there has been a lack of direct comparisons between species using comparable approaches. Part of the difficulty doing so is that studies of rodent and human place cells have often relied on different analysis methods, making it difficult to determine if and how place-cell properties differ between species. To address this, in this study, we set out to understand how differences in place cell detection methods affect the identified place cell populations and interpretations of spatial coding across species.To do so, we compared the most prevalent detection methods used in rodent and human research side by side, applying them to datasets from both species and to simulations. We found that different methods emphasize different features of spatial responses, which changes which neurons are identified as place cells. Across species, rat recordings revealed a wide range of spatial responses, from neurons with sharply localized activity to those with broader but reliable patterns. Human recordings, by contrast, were more concentrated at weaker but consistent levels of tuning. Importantly, these weaker but consistent responses reflect an overlapping population of neurons found in both species, which may serve similar functional roles in supporting flexible spatial memory and generalization. By separating methodological effects from biological differences, we lay the groundwork for future cross-species studies for spatial coding.Project Repository: This project is openly available through an online project repository, which includes all the code used for data pre-processing and analysis. Project Repository: https://github.com/HSUPipeline/PlaceCellMethods Dataset: This project uses electrophysiological data collected from neurosurgical patients, as well as an open-access dataset of rat recordings from CRCNS.org: http://dx.doi.org/10.6080/K09G5JRZ The human data were collected as part of a previously published study and will be made available prior to publication [1]. A custom simulation framework was developed to evaluate place cell detection methods across species and will be released as part of the open-source SpikeTools repository prior to publication. Software: All code used and developed for this project was written in the Python programming language. The code is openly available, licensed for reuse, and deposited in the project repository.Management of the dataset was conducted using the Human Single Unit (HSU) Pipeline: https://github.com/HSUPipeline Analyses of the single-neuron data were performed using the open-source SpikeTools toolbox: https://github.com/spiketools/spiketools Literature searches and related resources were organized using LISC, an open-source Python module for literature analysis. https://github.com/HSUPipeline/Literature.Materials Descriptions and Availability StatementsProject Repository: This project is openly available through an online project repository, which includes all the code used for data pre-processing and analysis. Project Repository: https://github.com/HSUPipeline/PlaceCellMethods Dataset: This project uses electrophysiological data collected from neurosurgical patients, as well as an open-access dataset of rat recordings from CRCNS.org: http://dx.doi.org/10.6080/K09G5JRZ The human data were collected as part of a previously published study and will be made available prior to publication [1]. A custom simulation framework was developed to evaluate place cell detection methods across species and will be released as part of the open-source SpikeTools repository prior to publication. Software: All code used and developed for this project was written in the Python programming language. The code is openly available, licensed for reuse, and deposited in the project repository.Management of the dataset was conducted using the Human Single Unit (HSU) Pipeline: https://github.com/HSUPipeline Analyses of the single-neuron data were performed using the open-source SpikeTools toolbox: https://github

Place cells, first identified in the rat hippocampus as neurons that fire selectively at specific locations, are central to investigations of the neural underpinnings of spatial navigation. With recent work with human patients, identifying and characterizing place cells across species has become increasingly important for understanding the extent to which decades of rodent research generalize to humans and uncovering principles of spatial cognition. One challenge, however, is that detection methods differ: rodent studies often rely on spatial information (SI), whereas human studies employ analysis of variance (ANOVA) - based approaches. These methodological differences may affect the identified place cell population, which complicates how their properties are interpreted and cross-species comparisons. To address this, we systematically applied multiple detection pipelines to human and rat datasets, supported by simulations that vary place-field properties. Our analyses and simulations demonstrate that spatial information and ANOVA-based approaches are responsive to distinct place field properties: spatial information primarily reflects the contrast between peak and average firing rates, while ANOVA emphasizes consistency across trials. Across species, rodent place cells revealed a broad spectrum of spatial tuning, including strongly tuned neurons with high spatial information (SI) and high ANOVA values. In contrast, human place cells lacked this strongly tuned population and exhibited a narrower distribution of tuning scores, concentrated at the lower end of both spatial tuning metrics. Despite these differences, both species had an overlapping population of neurons with weaker yet consistent spatial tuning, which may support important functional roles such as generalization and mixed selectivity. Together, our study provides a roadmap showing how spatial tuning metrics shape place cell detection and interpretation, while underscoring the functional importance of weaker-tuned neurons in cross-species comparisons.

While visual working memory (WM) is strongly associated with reductions in occipitoparietal 8-12 Hz alpha power, the role of 4-7 Hz frontal midline theta power is less clear, with both increases and decreases widely reported. Here, we test the hypothesis that this theta paradox can be explained by non-oscillatory, aperiodic neural activity dynamics. Because traditional time-frequency analyses of electroencephalopgraphy (EEG) data conflate oscillations and aperiodic activity, event-related changes in aperiodic activity can manifest as task-related changes in apparent oscillations, even when none are present. Reanalyzing EEG data from two visual WM experiments (n = 74), and leveraging spectral parameterization, we found systematic changes in aperiodic activity with WM load, and we replicated classic alpha, but not theta, oscillatory effects after controlling for aperiodic changes. Aperiodic activity decreased during WM retention, and further flattened over the occipitoparietal cortex with an increase in WM load. After controlling for these dynamics, aperiodic-adjusted alpha power decreased with increasing WM load. In contrast, aperiodic-adjusted theta power increased during WM retention, but because aperiodic activity reduces more, it falsely appears as though theta \"oscillatory\" power (e.g., bandpower) is reduced. Furthermore, only a minority of participants (31/74) had a detectable degree of theta oscillations. These results offer a potential resolution to the theta paradox where studies show contrasting power changes. We identify novel aperiodic dynamics during human visual WM that mask the potential role that neural oscillations play in cognition and behavior.While visual working memory (WM) is strongly associated with reductions in occipitoparietal 8-12 Hz alpha power, the role of 4-7 Hz frontal midline theta power is less clear, with both increases and decreases widely reported. Here, we test the hypothesis that this theta paradox can be explained by non-oscillatory, aperiodic neural activity dynamics. Because traditional time-frequency analyses of electroencephalopgraphy (EEG) data conflate oscillations and aperiodic activity, event-related changes in aperiodic activity can manifest as task-related changes in apparent oscillations, even when none are present. Reanalyzing EEG data from two visual WM experiments (n = 74), and leveraging spectral parameterization, we found systematic changes in aperiodic activity with WM load, and we replicated classic alpha, but not theta, oscillatory effects after controlling for aperiodic changes. Aperiodic activity decreased during WM retention, and further flattened over the occipitoparietal cortex with an increase in WM load. After controlling for these dynamics, aperiodic-adjusted alpha power decreased with increasing WM load. In contrast, aperiodic-adjusted theta power increased during WM retention, but because aperiodic activity reduces more, it falsely appears as though theta \"oscillatory\" power (e.g., bandpower) is reduced. Furthermore, only a minority of participants (31/74) had a detectable degree of theta oscillations. These results offer a potential resolution to the theta paradox where studies show contrasting power changes. We identify novel aperiodic dynamics during human visual WM that mask the potential role that neural oscillations play in cognition and behavior.Working Memory (WM) is our ability to hold information in mind without it being present in our external environment. Years of research focused on oscillatory brain dynamics to discover the mechanisms of WM. Here, we specifically look at oscillatory and non-oscillatory, aperiodic activity as measured with scalp EEG to test their significance in supporting WM. We challenge earlier findings regarding theta oscillations with our analysis approach, while replicating alpha oscillation findings. Furthermore, aperiodic activity is found to be involved in WM, over frontal regions in a task-general manner, and over anterior regions this activity is reduced with an increase the number of items that are remembered. Thus, we have identified novel aperiodic dynamics during human visual WM.Significance statementWorking Memory (WM) is our ability to hold information in mind without it being present in our external environment. Years of research focused on oscillatory brain dynamics to discover the mechanisms of WM. Here, we specifically look at oscillatory and non-oscillatory, aperiodic activity as measured with scalp EEG to test their significance in supporting WM. We challenge earlier findings regarding theta oscillations with our analysis approach, while replicating alpha oscillation findings. Furthermore, aperiodic activity is found to be involved in WM, over frontal regions in a task-general manner, and over anterior regions this activity is reduced with an increase the number of items that are remembered. Thus, we have identified novel aperiodic dynamics during human visual WM.