SMB2023 FollowThursday during the "CT03" time block. Room assignment: Barbie Tootle Room (#3156) in The Ohio Union.
University of Wisconsin Madison
"Multidimensionality in reinforcement learning models of human decision-making"
Temporal difference learning models, once developed as computer algorithms, have transformed how we study human decision-making and related brain activity. These models describe how a person updates their valuation of a decision according to an error in predicted rewards. While these valuations have conventionally been one-dimensional, recent experiments and theories suggest that these valuations might be multi-dimensional. In this talk, I will give a brief introduction to conventional modeling of human decision-making and discuss recent work to extend current reinforcement learning models to capture multi-dimensional valuations. Further, I will demonstrate the advantage of these extended models, from the perspective of what a person learns and the decisions they make, and connect these models to recent experiments. Last, I will discuss how these ideas can inform the design of future experiments
Additional authors: Joel Nishimura; Enkhzaya Enkhtaivan
The circadian (~24h) clock is based on a negative feedback loop centered around the PERIOD protein (PER) that is translated in the cytoplasm and then enters the nucleus to repress its own transcription at the right time of day. Such precise nucleus entry is mysterious because thousands of PER molecules transit through crowded cytoplasm and arrive at the perinucleus across several hours. To understand this, we developed a mathematical model describing the complex spatiotemporal dynamics of PER as a single random time delay. We find that the spatially coordinated bistable phosphoswitch of PER, which triggers the phosphorylation of accumulated PER at the perinucleus, leads to the synchronous and precise nuclear entry of PER. This leads to robust circadian rhythms even when PER arrival times are heterogenous and perturbed due to changes in cell crowdedness, cell size, and transcriptional activator levels. This shows how the circadian clock compensates for spatiotemporal noise.
Additional authors: Dae Wook Kim Seunggyu Lee Jae Kyoung Kim
Western Kentucky University
"A Comparison of Computational Perfusion Imaging Techniques."
Perfusion imaging is valuable because it is used to help grade tumors; differentiate between tumor types; differentiate tumors from nonneoplastic lesions; guide intraoperative sampling; most importantly, determine the efficacy of treatment. Computational techniques combined with the imaged data can help identify important biological parameters. For example, key parameters include cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) . These parameters can help distinguish between the likely salvageable tissue and irreversiblydamaged infarctcore.Theparametersarecalculateddeconvolvingcontrast-time curves with the arterial inlet input function. A common approach employed with the deconvolution method is a singular value decomposition (SVD). However, these algorithms are very sensitive to noise and artifacts in the source image which may introduce additional distortions in the output parameters. For this reason, we will employ machine learning algorithms to aid in the measurements of perfusion parameters from CT imaging and compare to parameter measurement using SVD with regularization.
Additional authors: Dr. Richard Schugart; Dr. Mark Robinson; Shake Ibna Abir
Iulia Martina Bulai
University of Sassari
"Wavelet packets and graph neuronal signal processing"
Nowadays graphs have become of significant importance given their use to describe complex system dynamics, with important applications to real world problems, e.g. graph representation of the brain, social networks, biological networks, spreading of a disease, etc.. In this work, , we introduce a novel graph wavelet packets construction, to our knowledge different from the ones known in literature. We get inspired by the Spectral Graph Wavelet Transform defined by Hammond et all. in , based on a spectral graph wavelet at scale s > 0, centered on vertex n, and a spectral graph scaling function, respectively. Moreover, after defining the wavelet packet spaces, and the associated tree, we obtain a dictionary of frames for R^N; with known lower and upper bounds. We will give some concrete examples on how the wavelet packets can be used for compressing, denoising and reconstruction by considering a signal, given by the fRMI (functional magnetic resonance imaging) data, on the nodes of voxel-wise brain graph G with 900760 nodes (representing the brain voxels) defined in -. References  D. K. Hammond, P. Vandergheynst , and R. Gribonval, Wavelets on graphs via spectral graph theory, Appl. Comput. Harmon. Anal. 30 (2011) 129-150.  A. Tarun, D. Abramian, M. Larsson, H. Behjat, and D. Van De Ville, Voxel-Wise Brain Graphs from Diffusion-Weighted MRI: Spectral Analysis and Application to Functional MRI, preprint (2021).  A. Tarun, H. Behjat, T. Bolton, D. Abramian, D. Van De Ville, Structural mediation of human brain activity revealed by white-matter interpolation of fMRI, NeuroImage 213 (2020) 116718. I.M. Bulai, S. Saliani, Spectral graph wavelet packets frames, Applied and Computational Harmonic Analysis (2023).
Additional authors: Sandra Saliani, University of Basilicata