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OpenBU is Boston University’s digital institutional repository for scholarly articles, theses and dissertations, preprints, and grey literature. This repository enables BU researchers to share, disseminate, and preserve their scholarship, and makes their research more accessible
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Recent Submissions
Capital income jumps and wealth distribution
(The Econometric Society, 2024) Benhabib, Jess; Cui, Wei; Miao, Jianjun
Compared to the distributions of earnings, the distributions of wealth in the US and many other countries are strikingly concentrated on the top and skewed to the right. To explain the income and wealth inequality, we provide a tractable heterogeneous‐agent model with incomplete markets in continuous time. We separate illiquid capital assets from liquid bond assets and introduce jump risks to capital income, which are crucial for generating a thicker tail of the wealth distribution than that of the labor income distribution. Under recursive utility, we derive optimal consumption and wealth in closed form and show that the stationary wealth distribution has an exponential right tail that closely approximates a power‐law distribution. Our calibrated model can match the income and wealth distributions in the US data including the extreme right tail of the wealth distribution.
Tracking the evolution of student interactions with an LLM-powered tutor
(ACM, 2024-03-18) Gold, Kevin; Geng, Shuang
Acoustic identification of the voicing boundary during intervocalic offsets and onsets based on vocal fold vibratory measures
(MDPI AG, 2021-05) Vojtech, Jennifer M.; Cilento, Dante D.; Luong, Austin T.; Noordzij, Jacob P.; Diaz-Cadiz, Manuel; Groll, Matti D.; Buckley, Daniel P.; McKenna, Victoria S.; Noordzij, J Pieter; Stepp, Cara E.
Methods for automating relative fundamental frequency (RFF)-an acoustic estimate of laryngeal tension-rely on manual identification of voiced/unvoiced boundaries from acoustic signals. This study determined the effect of incorporating features derived from vocal fold vibratory transitions for acoustic boundary detection. Simultaneous microphone and flexible nasendoscope recordings were collected from adults with typical voices (N=69) and with voices characterized by excessive laryngeal tension (N=53) producing voiced-unvoiced-voiced utterances. Acoustic features that coincided with vocal fold vibratory transitions were identified and incorporated into an automated RFF algorithm ("aRFF-APH"). Voiced/unvoiced boundary detection accuracy was compared between the aRFF-APH algorithm, a recently published version of the automated RFF algorithm ("aRFF-AP"), and gold-standard, manual RFF estimation. Chi-square tests were performed to characterize differences in boundary cycle identification accuracy among the three RFF estimation methods. Voiced/unvoiced boundary detection accuracy significantly differed by RFF estimation method for voicing offsets and onsets. Of 7721 productions, 76.0% of boundaries were accurately identified via the aRFF-APH algorithm, compared to 70.3% with the aRFF-AP algorithm and 20.4% with manual estimation. Incorporating acoustic features that corresponded with voiced/unvoiced boundaries led to improvements in boundary detection accuracy that surpassed the gold-standard method for calculating RFF.
Neurovascular impulse response function (IRF) during spontaneous activity differentially reflects intrinsic neuromodulation across cortical regions
(2024-09-15) Rauscher, Bradley C.; Fomin-Thunemann, Natalie; Kura, Sreekanth; Doran, Patrick R.; Perez, Pablo D.; Kılıç, Kıvılcım; Martin, Emily A.; Balog, Dora; Chai, Nathan X.; Froio, Francesca A.; Bloniasz, Patrick F.; Herrema, Kate E.; Tang, Rockwell; Knudstrup, Scott G.; Garcia, Andrew; Jiang, John X.; Gavornik, Jeffrey P.; Kleinfeld, David; Hasselmo, Michael E.; Lewis, Laura D.; Sakadzic, Sava; Tian, Lei; Mishne, Gal; Stephen, Emily P.; Thunemann, Martin; Boas, David A.; Devor, Anna
Ascending neuromodulatory projections from deep brain nuclei generate internal brain states that differentially engage specific neuronal cell types. Because neurovascular coupling is cell-type specific and neuromodulatory transmitters have vasoactive properties, we hypothesized that the impulse response function (IRF) linking spontaneous neuronal activity with hemodynamics would depend on neuromodulation. To test this hypothesis, we used optical imaging to measure (1) release of neuromodulatory transmitters norepinephrine (NE) or acetylcholine (ACh), (2) Ca2+activity of local cortical neurons, and (3) changes in hemoglobin concentration and oxygenation across the dorsal surface of cerebral cortex during spontaneous neuronal activity in awake mice. A canonical convolution model with a stationary IRF (e.g., the convolution kernel) describing evolution of total hemoglobin (HbT, reflective of dilation dynamics) with respect to Ca2+, resulted in a poor fit to the data. However, the HbT time-course was well predicted, pixel-by-pixel, by a weighted sum of Ca2+and NE time-courses. The weighting coefficients, calculated using linear regression, varied smoothly across the cortical space. Consistent with this result, modeling HbT as a weighted sum of stationary Ca2+- and NE-specific IRFs convolved with the respective time-courses dramatically improved the fit compared to the invariant IRF. In both the linear regression and the Double-IRF convolution models, Ca2+and NE weighting was positive and negative, respectively. In contrast to NE, ACh was largely redundant with Ca2+and therefore did not improve HbT estimation. Because NE covaried with arousal, we observed instances of the diminished hemodynamic coherence between cortical regions during high arousal despite coherent behavior of the underlying neuronal Ca2+activity. We conclude that while neurovascular coupling with respect to neuronal Ca2+is a dynamic and seemingly complex phenomenon, hemodynamic fluctuations can be captured by a simple linear model with stationary IRFs with respect to the underlying dilatory and constrictive forces. In the current study, these forces were captured by the positive Ca2+(dilation) and negative NE (constriction) coefficients. Without accounting for NE neuromodulation and the associated vasoconstriction, diminished hemodynamic coherence, commonly referred to as ″functional (dys)connectivity″ in BOLD fMRI studies, can be falsely interpreted as neuronal desynchronizations.
Sparse multi-task inverse covariance estimation for connectivity analysis in EEG source space
(IEEE, 2019-03) Liu, Feng; Stephen, Emily P.; Prerau, Michael J.; Purdon, Patrick L.
Understanding how different brain areas interact to generate complex behavior is a primary goal of neuroscience research. One approach, functional connectivity analysis, aims to characterize the connectivity patterns within brain networks. In this paper, we address the problem of discriminative connectivity, i.e. determining the differences in network structure under different experimental conditions. We introduce a novel model called Sparse Multi-task Inverse Covariance Estimation (SMICE) which is capable of estimating a common connectivity network as well as discriminative networks across different tasks. We apply the method to EEG signals after solving the inverse problem of source localization, yielding networks defined on the cortical surface. We propose an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve SMICE. We apply our newly developed framework to find common and discriminative connectivity patterns for α-oscillations during the Sleep Onset Process (SOP) and during Rapid Eye Movement (REM) sleep. Even though both stages exhibit a similar α-oscillations, we show that the underlying networks are distinct.
Propofol disrupts alpha dynamics in functionally distinct thalamocortical networks during loss of consciousness
(Proceedings of the National Academy of Sciences, 2023-03-14) Weiner, Veronica S.; Zhou, David W.; Kahali, Pegah; Stephen, Emily P.; Peterfreund, Robert A.; Aglio, Linda S.; Szabo, Michele D.; Eskandar, Emad N.; Salazar-Gomez, Andrés F.; Sampson, Aaron L.; Cash, Sydney S.; Brown, Emery N.; Purdon, Patrick L.
During propofol-induced general anesthesia, alpha rhythms measured using electroencephalography undergo a striking shift from posterior to anterior, termed anteriorization, where the ubiquitous waking alpha is lost and a frontal alpha emerges. The functional significance of alpha anteriorization and the precise brain regions contributing to the phenomenon are a mystery. While posterior alpha is thought to be generated by thalamocortical circuits connecting nuclei of the sensory thalamus with their cortical partners, the thalamic origins of the propofol-induced alpha remain poorly understood. Here, we used human intracranial recordings to identify regions in sensory cortices where propofol attenuates a coherent alpha network, distinct from those in the frontal cortex where it amplifies coherent alpha and beta activities. We then performed diffusion tractography between these identified regions and individual thalamic nuclei to show that the opposing dynamics of anteriorization occur within two distinct thalamocortical networks. We found that propofol disrupted a posterior alpha network structurally connected with nuclei in the sensory and sensory associational regions of the thalamus. At the same time, propofol induced a coherent alpha oscillation within prefrontal cortical areas that were connected with thalamic nuclei involved in cognition, such as the mediodorsal nucleus. The cortical and thalamic anatomy involved, as well as their known functional roles, suggests multiple means by which propofol dismantles sensory and cognitive processes to achieve loss of consciousness.
Latent neural dynamics encode temporal context in speech
(Elsevier BV, 2023-09-15) Stephen, Emily P.; Li, Yuanning; Metzger, Sean; Oganian, Yulia; Chang, Edward F.
Direct neural recordings from human auditory cortex have demonstrated encoding for acoustic-phonetic features of consonants and vowels. Neural responses also encode distinct acoustic amplitude cues related to timing, such as those that occur at the onset of a sentence after a silent period or the onset of the vowel in each syllable. Here, we used a group reduced rank regression model to show that distributed cortical responses support a low-dimensional latent state representation of temporal context in speech. The timing cues each capture more unique variance than all other phonetic features and exhibit rotational or cyclical dynamics in latent space from activity that is widespread over the superior temporal gyrus. We propose that these spatially distributed timing signals could serve to provide temporal context for, and possibly bind across time, the concurrent processing of individual phonetic features, to compose higher-order phonological (e.g. word-level) representations.
Electrographic seizures during low-current thalamic deep brain stimulation in mice
(Elsevier BV, 2024) Flores, Francisco J.; Dalla Betta, Isabella; Tauber, John; Schreier, David R.; Stephen, Emily P.; Wilson, Matthew A.; Brown, Emery N.
BACKGROUND: Deep brain stimulation of the central thalamus (CT-DBS) has potential for modulating states of consciousness, but it can also trigger electrographic seizures, including poly-spike-wave trains (PSWT). OBJECTIVES: To report the probability of inducing PSWTs during CT-DBS in awake, freely-moving mice. METHODS: Mice were implanted with electrodes to deliver unilateral and bilateral CT-DBS at different frequencies while recording electroencephalogram (EEG). We titrated stimulation current by gradually increasing it at each frequency until a PSWT appeared. Subsequent stimulations to test arousal modulation were performed at the current one step below the current that caused a PSWT during titration. RESULTS: In 2.21% of the test stimulations (10 out of 12 mice), CT-DBS caused PSWTs at currents lower than the titrated current, including currents as low as 20 μA. CONCLUSION: Our study found a small but significant probability of inducing PSWTs even after titration and at relatively low currents. EEG should be closely monitored for electrographic seizures when performing CT-DBS in both research and clinical settings.
State space oscillator models for neural data analysis
(IEEE, 2018-07) Beck, Amanda M.; Stephen, Emily P.; Purdon, Patrick L.
Neural oscillations reflect the coordinated activity of neuronal populations across a wide range of temporal and spatial scales, and are thought to play a significant role in mediating many aspects of brain function, including atten- tion, cognition, sensory processing, and consciousness. Brain oscillations are typically analyzed using frequency domain methods such as nonparametric spectral analysis, or time domain methods based on linear bandpass filtering. A typical analysis might seek to estimate the power within an oscillation sitting within a particular frequency band. A common approach to this problem is to estimate the signal power within that band, in frequency domain using the power spectrum, or in time domain by estimating the power or variance in a bandpass filtered signal. A major conceptual flaw in this approach is that neural systems, like many physiological or physical systems, have inherent broad-band 1/P' dynamics, whether or not an oscillation is present. Calculating power-in-band, or power in a bandpass filtered signal, can therefore be misleading, since such calculations do not distinguish between broadband power within the band of interest, and true underlying oscillations. In this paper, we present an approach for analyzing neural oscillations using a combination of linear oscillatory models. We estimate the parameters of these models using an expectation maximization (EM) algorithm, and employ AIC to select the appropriate model and identify the oscillations present in the data. We demonstrate the application of this method to univariate electroencephalogram (EEG) data recorded at quiet rest and during propofol-induced unconsciousness.
The project method and adolescent religious education
(Boston University, 1927) Blackmon, Ruth Emma