Short Bio

I am a research scientist at the University of Washington Institute for Learning and Brain Sciences, working primarily with human electrophysiological data: magnetoencephalography (MEG), electroencephalography (EEG), functional near-infared spectroscopy (fNIRS), and intracranial EEG (iEEG).

Previously:

Software

  • MNE - A complete package to process EEG and MEG data: forward and inverse problems, preprocessing, statistics, machine learning.
  • SciPy - A core scientific Python library, including signal processing routines.

Multiple other contributions to other open source scientific packages. More on my Github Page

Recent Publications

@article{bosseler_infants_2024,
 author = {Bosseler, A., Meltzoff, A., Bierer, S., Huber, E., Mizrahi, J., Larson, E., Endevelt-Shapira, Y., Taulu, S. and Kuhl, P.},
 doi = {10.1016/j.cub.2024.03.020},
 file = {Full Text PDF:/Users/larsoner/Zotero/storage/VAV9IDZ3/Bosseler et al. - 2024 - Infants’ brain responses to social interaction pre.pdf:application/pdf},
 issn = {0960-9822},
 journal = {Current Biology},
 keywords = {magnetoencephalography, MEG, social interaction, behavior, infant, attention, brain, language development, neuroscience, theta oscillations},
 language = {English},
 month = {April},
 note = {Publisher: Elsevier},
 number = {0},
 title = {Infants’ brain responses to social interaction predict future language growth},
 url = {https://www.cell.com/current-biology/abstract/S0960-9822(24)00317-8},
 urldate = {2024-04-08},
 volume = {0},
 year = {2024}
}

@article{yeatman_reading_2024,
 abstract = {Education sculpts specialized neural circuits for skills like reading that are critical to success in modern society but were not anticipated by the selective pressures of evolution. Does the emergence of brain regions that selectively process novel visual stimuli like words occur at the expense of cortical representations of other stimuli like faces and objects? “Neuronal Recycling” predicts that learning to read should enhance the response to words in ventral occipitotemporal cortex (VOTC) and decrease the response to other visual categories such as faces and objects. To test this hypothesis, and more broadly to understand the changes that are induced by the early stages of literacy instruction, we conducted a randomized controlled trial with pre-school children (five years of age). Children were randomly assigned to intervention programs focused on either reading skills or oral language skills and magnetoencephalography (MEG) data collected before and after the intervention was used to measure visual responses to images of text, faces, and objects. We found that being taught reading versus oral language skills induced different patterns of change in category-selective regions of visual cortex, but that there was not a clear tradeoff between the response to words versus other categories. Within a predefined region of VOTC corresponding to the visual word form area (VWFA) we found that the relative amplitude of responses to text, faces, and objects changed, but increases in the response to words were not linked to decreases in the response to faces or objects. How these changes play out over a longer timescale is still unknown but, based on these data, we can surmise that high-level visual cortex undergoes rapid changes as children enter school and begin establishing new skills like literacy.},
 author = {Yeatman, J., McCloy, D., Caffarra, S., Clarke, M., Ender, S., Gijbels, L., Joo, S., Kubota, E., Kuhl, P., Larson, E., O’Brien, G., Peterson, E., Takada, M. and Taulu, S.},
 doi = {10.1016/j.brainresbull.2024.110958},
 file = {ScienceDirect Snapshot:/Users/larsoner/Zotero/storage/UA7KMZAW/S0361923024000911.html:text/html},
 issn = {0361-9230},
 journal = {Brain Research Bulletin},
 keywords = {plasticity, category-selective, high-level visual cortex, neuronal recycling, reading, visual word form area (VWFA)},
 month = {April},
 pages = {110958},
 title = {Reading instruction causes changes in category-selective visual cortex},
 url = {https://www.sciencedirect.com/science/article/pii/S0361923024000911},
 urldate = {2024-05-01},
 year = {2024}
}

@article{yeo_effects_2023,
 abstract = {Objectives. We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM). Approach. A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.33 S m−1 was defined. The vertices were then randomly perturbed radially up to 2\%, 4\%, 6\%, 8\% and 10\% of the radius. For each head perturbation case, the forward signal was calculated for dipolar sources located at 2 cm, 4 cm, 6 cm and 8 cm from the origin (center of the sphere), and for a 324 sensor array located at 10 cm to 15 cm from the origin. Equivalent current dipole (ECD) source localization was performed for each of these forward signals. The signal for each perturbed spherical head case was then analyzed in the spatial frequency domain, and the signal and ECD errors were quantified relative to the unperturbed case. Main results. In the noiseless and high signal-to-noise ratio (SNR) case of approximately ≥6 dB, inaccuracies in our spherical BEM head conductor models lead to increased signal and ECD inaccuracies when sensor arrays are placed closer to the head. This is true especially in the case of deep and superficial sources. In the noisy case however, the higher SNR for closer sensor arrays allows for an improved ECD fit and outweighs the effects of head geometry inaccuracies. Significance. OPMs may be placed directly on the head, as opposed to the more commonly used superconducting quantum interference device sensors which must be placed a few centimeters away from the head. OPMs thus allow for signals of higher spatial resolution to be captured, resulting in potentially more accurate source localizations. Our results suggest that an increased emphasis on accurate head modeling for OPMs may be necessary to fully realize its improved source localization potential.},
 author = {Yeo, W., Larson, E., Iivanainen, J., Borna, A., McKay, J., Stephen, J., Schwindt, P. and Taulu, S.},
 doi = {10.1088/1361-6560/accc06},
 file = {IOP Full Text PDF:/Users/larsoner/Zotero/storage/LEYZEZG5/Yeo et al. - 2023 - Effects of head modeling errors on the spatial fre.pdf:application/pdf},
 issn = {0031-9155},
 journal = {Physics in Medicine \& Biology},
 language = {en},
 month = {April},
 note = {Publisher: IOP Publishing},
 number = {9},
 pages = {095022},
 title = {Effects of head modeling errors on the spatial frequency representation of {MEG}},
 url = {https://dx.doi.org/10.1088/1361-6560/accc06},
 urldate = {2023-05-12},
 volume = {68},
 year = {2023}
}

@article{sheffield_sound_2023,
 abstract = {Functional near-infrared spectroscopy (fNIRS) is a viable non-invasive technique for functional neuroimaging in the cochlear implant (CI) population; however, the effects of acoustic stimulus features on the fNIRS signal have not been thoroughly examined. This study examined the effect of stimulus level on fNIRS responses in adults with normal hearing or bilateral CIs. We hypothesized that fNIRS responses would correlate with both stimulus level and subjective loudness ratings, but that the correlation would be weaker with CIs due to the compression of acoustic input to electric output.},
 author = {Sheffield, S., Larson, E., Butera, I., DeFreese, A., Rogers, B., Wallace, M., Stecker, G., Lee, A. and Gifford, R.},
 doi = {10.1007/s10548-023-00981-w},
 issn = {1573-6792},
 journal = {Brain Topography},
 keywords = {Functional near-infrared spectroscopy, Cochlear implants, Loudness ratings, Stimulus level},
 language = {en},
 month = {September},
 number = {5},
 pages = {686--697},
 title = {Sound {Level} {Changes} the {Auditory} {Cortical} {Activation} {Detected} with {Functional} {Near}-{Infrared} {Spectroscopy}},
 url = {https://doi.org/10.1007/s10548-023-00981-w},
 urldate = {2024-04-08},
 volume = {36},
 year = {2023}
}

@article{mittag_reduced_2022,
 abstract = {Research on children and adults with developmental dyslexia—a specific difficulty in learning to read and spell—suggests that phonological deficits in dyslexia are linked to basic auditory deficits in temporal sampling. However, it remains undetermined whether such deficits are already present in infancy, especially during the sensitive period when the auditory system specializes in native phoneme perception. Because dyslexia is strongly hereditary, it is possible to examine infants for early predictors of the condition before detectable symptoms emerge. This study examines low-level auditory temporal sampling in infants at risk for dyslexia across the sensitive period of native phoneme learning. Using magnetoencephalography (MEG), we found deficient auditory sampling at theta in at-risk infants at both 6 and 12 months, indicating atypical auditory sampling at the syllabic rate in those infants across the sensitive period for native-language phoneme learning. This interpretation is supported by our additional finding that auditory sampling at theta predicted later vocabulary comprehension, nonlinguistic communication and the ability to combine words. Our results indicate a possible early marker of risk for dyslexia.},
 author = {Mittag, M., Larson, E., Taulu, S., Clarke, M. and Kuhl, P.},
 copyright = {http://creativecommons.org/licenses/by/3.0/},
 doi = {10.3390/ijerph19031180},
 file = {Full Text PDF:/Users/larsoner/Zotero/storage/9XPFB5TA/Mittag et al. - 2022 - Reduced Theta Sampling in Infants at Risk for Dysl.pdf:application/pdf;Full Text PDF:/Users/larsoner/Zotero/storage/EVUFPNMT/Mittag et al. - 2022 - Reduced Theta Sampling in Infants at Risk for Dysl.pdf:application/pdf;Snapshot:/Users/larsoner/Zotero/storage/UIGLE4ZA/1180.html:text/html},
 issn = {1660-4601},
 journal = {International Journal of Environmental Research and Public Health},
 keywords = {auditory, dyslexia, infant, MEG, temporal sampling},
 language = {en},
 month = {January},
 note = {Number: 3
Publisher: Multidisciplinary Digital Publishing Institute},
 number = {3},
 pages = {1180},
 title = {Reduced {Theta} {Sampling} in {Infants} at {Risk} for {Dyslexia} across the {Sensitive} {Period} of {Native} {Phoneme} {Learning}},
 url = {https://www.mdpi.com/1660-4601/19/3/1180},
 urldate = {2022-07-28},
 volume = {19},
 year = {2022}
}

@article{butera_functional_2022,
 abstract = {Visual cues are especially vital for hearing impaired individuals such as cochlear implant (CI) users to understand speech in noise. Functional Near Infrared Spectroscopy (fNIRS) is a light-based imaging technology that is ideally suited for measuring the brain activity of CI users due to its compatibility with both the ferromagnetic and electrical components of these implants. In a preliminary step toward better elucidating the behavioral and neural correlates of audiovisual (AV) speech integration in CI users, we designed a speech-in-noise task and measured the extent to which 24 normal hearing individuals could integrate the audio of spoken monosyllabic words with the corresponding visual signals of a female speaker. In our behavioral task, we found that audiovisual pairings provided average improvements of 103\% and 197\% over auditory-alone listening conditions in −6 and −9 dB signal-to-noise ratios consisting of multi-talker background noise. In an fNIRS task using similar stimuli, we measured activity during auditory-only listening, visual-only lipreading, and AV listening conditions. We identified cortical activity in all three conditions over regions of middle and superior temporal cortex typically associated with speech processing and audiovisual integration. In addition, three channels active during the lipreading condition showed uncorrected correlations associated with behavioral measures of audiovisual gain as well as with the McGurk effect. Further work focusing primarily on the regions of interest identified in this study could test how AV speech integration may differ for CI users who rely on this mechanism for daily communication.},
 author = {Butera, I., Larson, E., DeFreese, A., Lee, A., Gifford, R. and Wallace, M.},
 doi = {10.1007/s10548-022-00904-1},
 file = {Full Text PDF:/Users/larsoner/Zotero/storage/A4GSAUCM/Butera et al. - 2022 - Functional localization of audiovisual speech usin.pdf:application/pdf},
 issn = {1573-6792},
 journal = {Brain Topography},
 keywords = {Multisensory integration, fNIRS, Infrared spectroscopy, McGurk effect, Speech in noise},
 language = {en},
 month = {July},
 number = {4},
 pages = {416--430},
 title = {Functional localization of audiovisual speech using near infrared spectroscopy},
 url = {https://doi.org/10.1007/s10548-022-00904-1},
 urldate = {2022-09-09},
 volume = {35},
 year = {2022}
}

@article{rockhill_intracranial_2022,
 abstract = {Rockhill et al., (2022). Intracranial Electrode Location and Analysis in MNE-Python. Journal of Open Source Software, 7(70), 3897, https://doi.org/10.21105/joss.03897},
 author = {Rockhill, A., Larson, E., Stedelin, B., Mantovani, A., Raslan, A., Gramfort, A. and Swann, N.},
 doi = {10.21105/joss.03897},
 file = {Full Text PDF:/Users/larsoner/Zotero/storage/4RG7IV4Q/Rockhill et al. - 2022 - Intracranial Electrode Location and Analysis in MN.pdf:application/pdf},
 issn = {2475-9066},
 journal = {Journal of Open Source Software},
 language = {en},
 month = {February},
 number = {70},
 pages = {3897},
 title = {Intracranial {Electrode} {Location} and {Analysis} in {MNE}-{Python}},
 url = {https://joss.theoj.org/papers/10.21105/joss.03897},
 urldate = {2024-05-30},
 volume = {7},
 year = {2022}
}

Full list of publications