The importance of family history in dyslexia.
Lasnick, O., Feng, J., Quirion, A., Hart, S.A., Hoeft, F. (2022). The importance of family history in dyslexia. The Reading League journal, 3(2), 35-42.
Curriculum Vitae
Education
PhD in Language & Cognition (Psychological Sciences)
University of Connecticut
2021 - 2024
MSc in Language & Cognition (Psychological Sciences)
University of Connecticut
2019 - 2021
BA in Cognitive Science, Computer Science
University of California at Berkeley
2015 - 2019
Research Experience
Researcher @ Stanford Cognitive & Systems Neuroscience Laboratory, Stanford University
Supervisor: Vinod Menon, PhD
2024 - Present
Researcher (PhD-track) @ brainLENS (Learning, Engineering, & Neural Systems) Laboratory, University of Connecticut
Supervisor: Fumiko Hoeft, MD PhD
2019 - 2024
Research Assistant @ Language & Cognitive Development Laboratory, UC Berkeley
Supervisor: Mahesh Srinivasan, PhD
2018 - 2019
Projects & publications
Event-Related Potential Studies of Reading in Relation to Developmental Dyslexia: A Systematic Review.
[Preregistration] Clement-Lam, S. S.-Y., Lasnick, O., Mitra, A., Kinnie, B., Lyon, C., Luo, J., Kearns, D., Hoeft, F. (2022, May 30). Event-Related Potential Studies of Reading in Relation to Developmental Dyslexia: A Systematic Review. OSF Preregistration: https://osf.io/dbgc3.
Using Genetic Similarity Quantified by Kinship Coefficients to Investigate Familial Contributions to Reading Disorder.
[Preregistration] Lasnick, O.H.M. (2023, August 7). Using Genetic Similarity Quantified by Kinship Coefficients to Investigate Familial Contributions to Reading Disorder. OSF Preregistration: https://doi.org/10.17605/OSF.IO/3H6PT.
Left-dominance for resting-state temporal low-gamma power in children with impaired word-decoding and without comorbid ADHD.
Lasnick, O., Hancock, R., Hoeft, F. (2023). Left-dominance for resting-state temporal low-gamma power in children with impaired word-decoding and without comorbid ADHD. PloS one, 18(12), e0292330. https://doi.org/10.1371/journal.pone.0292330.
Sensory temporal sampling in time: an integrated model of the TSF and neural noise hypothesis as an etiological pathway for dyslexia.
Lasnick, O.H.M., Hoeft, F. (2024). Sensory temporal sampling in time: an integrated model of the TSF and neural noise hypothesis as an etiological pathway for dyslexia. Frontiers in human neuroscience, 17, 1294941. https://doi.org/10.3389/fnhum.2023.1294941.
Modeling delays in neurodevelopmental maturity of the reading network using support vector regression on functional connectivity data.
[In prep] Lasnick, O., Kamal, S., Marrouch, N., Low, S, Hoeft, F. Modeling delays in neurodevelopmental maturity of the reading network using support vector regression on functional connectivity data.
Selected talks, presentations, & posters
[Poster] Lasnick, O., Luo, J., Kinnie, B., Kamal, S., Low, S., Marrouch, N., Hoeft, F. Do estimates of age based on functional connectivity differ as a function of reading ability? Organization for Human Brain Mapping (OHBM) Conference in Seoul, June 2024.
[Poster] Lasnick, O., Hoeft, F. Behavioral and cortical profiles for family history of dyslexia using binary, continuous, and genetic kinship approaches. Cognitive Neuroscience Society (CNS) Annual Meeting in Toronto, April 2024.
[Poster] Clement-Lam, S. S.-Y., Lasnick, O., Mitra, A., Kinnie, B., Lyon, C., Luo, J., Kearns, D., Hoeft, F. ERP studies of reading in relation to developmental dyslexia: a systematic review. FLUX: The Society for Developmental Cognitive Neuroscience Conference in Santa Rosa, September 2023.
Awards, fellowships, & grants
2022 – 2024, F31HD107944 (PI Lasnick). Ruth L. Kirschstein National Research Service Award (NRSA) Individual Predoctoral Fellowship (Parent F31), National Institutes of Health. “Using genetic similarity quantified by kinship coefficients to investigate familial contributions to reading disorder”.
Total awarded: $83,404
2021 – 2022, NRT-UtB 1735225 (PI Magnuson). National Science Foundation Research Traineeship, National Science Foundation. “Science of learning, from neurobiology to real-world application: a problem-based approach (SLAC program)”.
2019 – 2021, T32DC017703 (Multi-PIs Eigsti/Myers). National Institutes of Health Training Grant, National Institutes of Health. “University of Connecticut Cognitive Neuroscience of Communication – Connecticut (CNC-CT): Training in the Cognitive Neuroscience of Communication”.
2015 – 2019, UC Berkeley Undergraduate Scholarship. Awarded based on entering with a high school GPA of at least 4.1; awarded during continuing semesters with at least a 3.5 university GPA.
Teaching experience
General skills & technical knowledge
Data analysis
Data visualization Big data Programming High-performance computing Project management Docker containers Writing reports
Computational & statistical modeling Behavioral assessment & psychometrics Machine learning Database management Testing (unit tests & integration tests) Data quality assessment Version control (Git)
Programming languages
Python (numpy, scikit-learn, scipy, pandas)
R/RStudio (ggplot2) SQL/MySQL Java C/C++ Scheme HTML
Neuroimaging tools
MRI/fMRI/MRS (data collection)
FreeSurfer
FSL
fMRIPrep
CONN functional connectivity toolbox
EEGLAB
Automagic
MNE-Python
Statistical tools & software
SPSS
R/RStudio
JASP
MATLAB
Microsoft Excel
Other
LaTeX
Regex
Markdown