Jasper Laca
LA-BASED REsearcher and phD applicant
Jasper (he/him) is an LA-based researcher and PhD applicant. He has a host of useful work experience including, but not limited to: programming (python, JS, R, shell), neuroimaging, complex data analyses, bio-specimen collection and preparation, multi-site IRB agreements, business management, media-production, photography, lab management, executive assistance, child-care, customer service, IT, communications, social media, full-stack web development, A/V production, and much more.
Notably, he has worked at three academic institutions as a researcher and lab member:
Occidental College
University of Southern California’s Keck School of Medicine
University of California, Los Angeles.
He currently works at the University of California, Los Angeles as a Policy Analyst and Research Assistant in several psychology labs.
His aspirations lie in Clinical, Cognitive, and Developmental psychologies, Neuroscience, and Medicine.
Curriculum Vitae,
at a glance
Education History:
Bachelor of Arts with Honors: Psychology
Year of Graduation: 2018
Senior Honors Research: Caffeine’s Effect on Coexistent Reasoning in a Sentence Verification Task
Minor in Media, Arts, and Culture
Academic Advisor: Andrea Hopmeyer, Ph.D.
Research Advisor: Andrew Shtulman, Ph.D.
GPA: 3.48
Research Interest:
Discovering novel ways to interpret functional MRI data, with the goal of elucidating the neural basis of mental health problems. Specifically, I am interested in using fMRI as a tool in the diagnosis and treatment of certain psychopathologies, including Depression, Anxiety, and other related disorders.
First authorship:
Laca, J., Kocielnik, R., Nguyen, J. H., You, J., Tsang, R., Wong, E. Y., Shtulman, A., Anandkumar, A., Hung, A. J. Using Real-time Feedback To Improve Surgical Performance on a Robotic Tissue Dissection Task. European Urology Open Science. 46, 15-21 (2022). https://doi.org/10.1016/j.euros.2022.09.015
First authorship (conference abstracts):
Laca, J., Laca, E., Ho, T. C. Age- and Sex-Dependent Effects of Global and Within-Network Integration in Sleep-Deprived Versus Well-Rested Individuals: Application of Functional Principal Component Analysis on Resting-State fMRI Data, Flux 2024 September 28-30, Flux Congress, Society for Developmental and Cognitive Neuroscience, 2024. https://pmg.joynadmin.org/documents/1093/66e356347e9eb7544b2adf53.pdf
Laca, J., Kiyasseh, D., Kocielnik, R., Haque, T. F., Ma, R., Anandkumar, A., Hung, A. J. AI-BASED VIDEO FEEDBACK TO IMPROVE NOVICE PERFORMANCE ON A ROBOTIC SUTURING TASK. The Journal of Urology. 209, Supp. 4. (2023/4). https://doi.org/10.1097/JU.0000000000003316.05
Laca, J., Kocielnik, R., Nguyen, J. H., Tsang, R., You, J., Hui, A., Anandkumar, A., Hung, A. J. BUILDING A REAL-TIME FEEDBACK SYSTEM TO IMPROVE PERFORMANCE ON A ROBOTIC TISSUE DISSECTION TASK. The Journal of Urology. 207, Supp. 5. (2022/5). https://doi.org/10.1097/JU.0000000000002532.15
Current project:
Parsing resting state networks with FPCA:
Age-dependent effects of MDD on Adolescent Resting-state Networks
Slide deck sharing preliminary findings
FLUX 2024 Poster PResentation
Short synopsis:
This study applied functional principal component analysis (fPCA) to resting-state fMRI data from the Stockholm SleepyBrain Project to explore brain connectivity patterns. The analysis included 84 participants subjected to both sleep deprivation and unrestricted sleep, aiming to detect age and sex-related differences. The results revealed that fewer functional principal components (fPCs) were needed to explain brain signal variance during sleep deprivation, suggesting greater functional cohesion. Age-related effects were observed, with younger participants showing greater within-network integration (WNI) in several networks, and females generally displaying higher WNI across networks compared to males. This demonstrates the potential of fPCA as a sensitive tool for fMRI analysis.
Full ABSTRACT:
Background: Functional principal component analysis (fPCA) is a statistical tool used for analyzing functional data, where observations are functions rather than discrete values. In contrast to conventional assessments of functional connectivity patterns, which predominantly use pairwise correlations, fPCA offers an alternative framework to understand brain connectivity by partitioning it into orthogonal functional components (fPCs), representing its main modes of variation. The implications of using this method to identify age-related effects of clinical biomarkers is therefore enormous.
Methods: We explored fPCA to analyze resting-state fMRI data in an open-source dataset, The Stockholm SleepyBrain Project, where each of 84 participants (ages 20-75; 44 female) was tested after partial sleep deprivation (SD, 3 hours of sleep) and after unrestricted sleep (WR) in a crossover design and underwent MR scanning during each condition (152 scans total). Each MR session included one 8-minute resting-state fMRI scan (TR=2.5 s) with eyes open. Previous analysis of this data failed to detect significant SD effects on brain connectivity, offering an opportunity to highlight the sensitivity of fPCA analyses to perturbations in brain state. As age and sex have also consistently been strong predictors of resting-state fMRI patterns, we sought to test what effects, if any, they had on the fPCA results. Resting-state fMRI data were aggregated and preprocessed with FSL 6.0.7.5, including skull stripping and registration to the Harvard Oxford Cortical Atlas (48 cortical regions). Voxel values were averaged by region, yielding 48 time-series per participant, per session. The time-series were then standardized within each subject’s region and session and subjected to fPCA with the 'fdapace' package in R. Loadings (correlations) of each region on each of the first 3 fPCs (equivalent to ~ 75% avg. cumulative fraction of explained variance, FVE) were averaged by 11 resting-state networks (defined a priori by the authors). Absolute values of averages were used as an index of within-network integration (WNI). WNI was analyzed with a mixed-effects model including sex, age, sleep deprivation condition, and network as fixed effects, using subject and session within subject as random effects. Networks that are highly integrated have regions with loadings of equal sign and large absolute value on the same fPC.
Results: On average, 11.2 fPCs explained 99.3% of total signal variance (FVE) under SD whereas 13.1 fPCs were necessary to reach the same threshold for those who were WR (p=0.05). This result may reflect a greater overall functional cohesion or global network integration in the cortex during SD, consistent with previous research. The first fPC, or mode of variation, showed that average overall WNI tended to be lower for SD subjects, compared to their WR counterparts (p=0.084). Additionally, a significant interaction effect was observed between age and network (p=0.013, F(10,1480)=2.26). Younger subjects had greater WNI than their older counterparts in the auditory (p=0.05, t(270)=1.97), default mode (p=0.077, t(270)=1.78), and salience networks (p=0.006, t(270)=2.78) The second fPC showed a significant main effect of age (p=0.016, F(1,80)=6.02), indicating that younger subjects tended to have greater overall WNI compared to the older subjects in the study. Finally, the third fPC exhibited a significant interaction effect between SD-WR and network (p=0.013, F(10,1480)=2.24), mostly due to the WR group exhibiting greater WNI in auditory (p=0.088, t(517)=1.71), language (p=0.020, t(517)=2.34), salience (p=0.041, t(517)=2.05), and sensorimotor networks (p=0.003, t(517)=3.03) compared to the SD. Interestingly, in this fPC, females had more WNI, across all networks, than males (p=0.052, F(1,145)=3.84).
Discussion: These results serve as a proof-of-concept for the use of fPCA in fMRI analysis as a novel tool to understand psychological mechanisms and conditions affecting the developing brain.