Agenda
Duration | Sessions |
2:00 -2:10 | SPECTRA student chapter introduction |
2:10 – 3:10 | Keynote Lecture: Arjun Yodh, PhD, Professor Department of Physics and Astronomy University of Pennsylvania |
3:10 – 4:00 | Student talks: Jie Liao, Postdoc, Micro/Nano Photonics Lab Department of Electrical and System Engineering Dalin Yang, Postdoc, Brain Light Lab Mallinckrodt Institute of Radiology Shenxuan Chen, PhD candidate, NeuroPhoto Lab Department of Biomedical Engineering |
4:00 – 4:10 | break |
4:10 – 5:10 | Faculty talks: Christine M. O’Brien, PhD, Assistant Professor Department of Biomedical Engineering Ikbal Şencan-Eğilmez, PhD, Assistant Professor Mallinckrodt Institute of Radiology |
5:10 – 6:30 | Poster session & Reception — award, food, and drinks |
Keynote Lecture
2:10 – 3:10 pm
Arjun G. Yodh is the James M. Skinner Professor of Science and Chair of the Department of Physics and Astronomy at the University of Pennsylvania (Penn). He was recently Director (2009-2020) of Penn’s Materials Science and Engineering Center (NSF-MRSEC) and its Laboratory for the Research on the Structure of Matter. Areas of his current research include: Biophotonics, especially functional imaging and monitoring of living tissues with diffuse light for which he was awarded the 2021 Feld Biophotonics Prize of the Optical Society of America (now Optica), the physics of Soft Matter such as colloids and liquid crystals, and Optical Sciences. In addition to mentoring more than one hundred PhD students and post-doctoral associates, Yodh has made significant contributions to education and outreach at Penn, for example leading partnerships with the University of Puerto Rico and research experience programs for undergraduates and high school students/teachers. Yodh graduated from Cornell University (1981) with a B.Sc. in Applied & Engineering Physics. He obtained his Ph.D. from Harvard (1986) under the guidance of Tom Mossberg, and he then spent two years at AT&T Bell Laboratories as a post-doc working with Steven Chu and Harry Tom. Yodh joined the faculty at Penn in 1988, where he has remained for his entire career.
Deep Tissue Biophotonics with Diffusing Light
Diffusing light in the near infrared spectral part of the spectrum can be used to quantitatively probe physiology of tissues located far below body surfaces. After traversing through tissue, the emerging light fields contain a wealth of diagnostic information about blood flow, blood oxygenation, and oxygen metabolism, about molecular biomarkers such as cytochrome-C oxidase, lipid, and water. Health biometrics can then be derived from these data based on optics alone or in combination with other signals. I will introduce the essential diffuse optics measurement tools and paradigms, many of which were hatched in the physics and bioengineering communities. Then I will discuss selected clinical and pre-clinical examples that we have investigated with our collaborators. Depending on time, this will include studies of brain metabolism and autoregulation, cancer monitoring, placental health, and more.
Student Talks
3:10 – 4:00 pm
Jie Liao, PhD
Dalin Yang, PhD
Shenxuan Chen
Micro and nanoscale particles play pivotal roles across diverse fields, from biomedical imaging and environmental processes to early disease diagnosis, influencing numerous scientific research and industrial applications. Their unique characteristics demand accurate detection, characterization, and identification. Optical microsensors have emerged as a powerful technology in the past two decades, offering high sensitivity and compact design, enabling single-molecule detection through surface binding monitored via evanescent fields.
To further enhance the sensing capability, we design and demonstrate an optofluidic, high throughput, ultra-sensitive optical microresonator sensor that can capture subtle acoustic signals, analogous to whispers, generated by tiny particles from the absorption of pulsed light energy, providing photoacoustic spectroscopy information for real-time, label-free detection and interrogation of particles and cells in their native solution environments across an extended sensing volume. Leveraging unique optical absorption of the targets, our technique can selectively detect and classify particles flowing through the sensor systems without the need for surface binding, even in a complex sample matrix, such as whole blood samples. We showcase the measurement of gold nanoparticles with diverse geometries and different species of red blood cells in the presence of other cellular elements and a wide variety of proteins. These particles are effectively identified and classified based on their photoacoustic fingerprint that captures particle shape, composition, molecule properties, and morphology features.
This work opens up new avenues to achieve rapid, reliable, and high-throughput particle and cell identification in clinical and industrial applications, offering a valuable tool for understanding complex biological and environmental systems.
Background
Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.
Methods
We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.
Results
We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.
Limitations
Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.
Conclusions
This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.
Functional connectivity (FC) quantifies the synchronization between brain regions or neurons using Pearson correlation, and it is an important biomarker for various neurological diseases. However, Pearson correlation only characterizes linear dependence, while neuron-level activities usually present nonlinear dependence. How nonlinear dependence may contribute to neuron-level connections has not been well studied. In this report, we applied mutual information (MI) to quantify the nonlinear dependence between neuronal activities of about 1000 neurons recorded using a large field-of-view two-photon microscope. The positive connected neurons revealed by correlation-based FC maps were also revealed by the MI-based FC maps, indicating MI is capable of quantifying dependence between neuron activities. More importantly, the MI-based FC maps reveal connections that correlation-based FC maps do not. These results highlight the importance of considering nonlinear dependence when analyzing neuron-level connections. We think that studying the nonlinear dependence between neuronal activities will help us have a deeper understanding of FC and its roles in neurological diseases.
Faculty Talks
4:10 – 5:10 pm
Poster Session and Reception
5:10 – 6:30 pm
Awards, Food, Drinks will be provided. We are looking for submissions using this link.
Posters with the most student votes will be awarded the Best Poster Presentation Awards.