Brain large-scale dynamics is constrained by the heterogeneity of intrinsic anatomical substrate. Little is known how the spatiotemporal dynamics adapt for the heterogeneous structural connectivity (SC). Modern neuroimaging modalities make it possible to study the intrinsic brain activity at the scale of seconds to minutes. Diffusion magnetic resonance imaging (dMRI) and functional MRI reveals
the large-scale SC across different brain regions. Electrophysiological methods (i.e. MEG/EEG) provide direct measures of neural activity and exhibits complex neurobiological temporal dynamics which could not be solved by fMRI. However, most of existing multimodal analytical methods collapse the brain measurements either in space or time domain and fail to capture the spatio-temporal circuit dynamics. In this paper, we propose a novel spatio-temporal graph Transformer model to integrate the structural and functional connectivity in both spatial and temporal domain. The proposed method learns the heterogeneous node and graph
representation via contrastive learning and multi-head attention based graph Transformer using multimodal brain data (i.e. fMRI, MRI, MEG and behavior performance). The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structurefunction interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method could highlight the local properties of large-scale brain spatio-temporal dynamics and capture the dependence strength between functional connectivity
and behaviors. In summary, the proposed method enables the complex brain dynamics explanation for different modal variants.
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Task Decomposition for MPC: A Computationally Efficient Approach for Linear Time-Varying Systems
A Task Decomposition method for iterative learning Model Predictive Control (TDMPC) for linear time-varying systems is presented. We consider the availability of state- input trajectories which solve an original task T1, and design a feasible MPC policy for a new task, T2, using stored data from T1. Our approach applies to tasks T2 which are composed of subtasks contained in T1. In this paper we formally define the task decomposition problem, and provide a feasibility proof for the resulting policy. The proposed algorithm reduces the computational burden for linear time-varying systems with piecewise convex constraints. Simulation results demonstrate the improved efficiency of the proposed method on a robotic path-planning task.
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- Award ID(s):
- 1931853
- NSF-PAR ID:
- 10176527
- Date Published:
- Journal Name:
- IFACPapersOnLine
- ISSN:
- 2405-8963
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background CD8+T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8+T‐cell expression can better identify the population benefitting from immunotherapy.
Purpose To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8+T‐cell expression in patients with PDAC.
Study Type Retrospective cohort study.
Population Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts.
Field Strength/Sequence/3 T Breath‐hold single‐shot fast‐spin echo T2‐weighted sequence and noncontrast T1‐weighted fat‐suppressed sequences.
Assessment CD8+T‐cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1‐ and T2‐weighted images and reduced using the Wilcoxon rank‐sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility.
Statistical Tests Kaplan–Meier estimates, Student's t‐test, the Kruskal–Wallis H test, and the chi‐square test, receiver operating characteristic curve, and decision curve analysis.
Results A log‐rank test showed that the survival duration in the CD8‐high group (25.51 months) was significantly longer than that in the CD8‐low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77–0.92) and 0.69 (95% CI, 0.53–0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77–0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61–0.87).
Data Conclusions This study developed a noncontrast MRI‐based radiomics model that can preoperatively determine CD8+T‐cell expression in patients with PDAC and potentially immunotherapy planning.
Evidence Level 5
Technical Efficacy Stage 2
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In 1990-1991 segments of boles from felled sugar maple (Acer saccharum), yellow birch (Betula alleghaniensis) and American beech (Fagus grandifolia) trees were placed in the field to study the rate of decomposition and nutrient loss (or gain) over time. The segments incubated in the field, ranging from 0.5-1.3 meters in length, were paired with fresh segments from the same trees. The fresh segments were taken to the lab shortly after felling, dried, weighed and subsampled. Fresh samples of wood and bark were collected separately. Incubated bole segments were collected in 1993 (T1), 1997 (T2), 2001 (T3), 2007 (T4) and 2015/2016 (T5). The whole bole segments were transported to the lab, measured, dried and weighed to determine mass loss. Subsamples of the bole wood and bark were collected for chemical analysis, including C, N, H, Ca, Mg, K, Si, Al, Pb, Zn, Mn and Fe. Chemical analyses were conducted concurrently on the fresh (T0) and incubated samples. This data set includes the masses of the fresh and incubated boles along with the concentrations of the chemical analytes. Element pools in the boles can be calculated by multiplying the concentrations by the mass values. This data set includes chemical data for samples collected in 1993, 2001, and 2007 and their paired fresh samples. Samples from 1997 were measured for mass, but inadvertently discarded prior to chemical analysis. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.more » « less
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In many real-world applications of monitoring multivariate spatio-temporal data that are non-stationary over time, one is often interested in detecting hot-spots with spatial sparsity and temporal consistency, instead of detecting system-wise changes as in traditional statistical process control (SPC) literature. In this paper, we propose an efficient method to detect hot-spots through tensor decomposition, and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model decomposing the original data into: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate model parameters by the penalized framework that includes Least Absolute Shrinkage and Selection Operator (LASSO) and fused LASSO penalty. An efficient recursive optimization algorithm is developed based on Fast Iterative Shrinkage Thresholding Algorithm (FISTA). Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor model residuals after removing global means, which helps to detect when and where hot-spots occur. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods including scan statistics, LASSO-based, PCA-based, T2-based control chart in extensive numerical simulation studies and a real crime rate dataset.more » « less
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Interstitial patterning of nuclear spins is a nascent design principle for controlling electron spin superposition lifetimes in open-shell complexes and solid-state defects. Herein we report the first test of the impact of the patterning principle on ligand-based nuclear spin dynamics. We test how substitutional patterning of 1H and 79/81Br nuclear spins on ligands modulates proton nuclear spin dynamics in the ligand shell of metal complexes. To do so, we studied the 1H nuclear magnetic resonance relaxation times (T1 and T2) of a series of eight polybrominated catechol ligands and six complexes formed by coordination of the ligands to a Ti(IV) ion. These studies reveal that 1H T1 values can be enhanced in the individual ligands by a factor of 4 (from 10.8(3) to 43(5) s) as a function of substitution pattern, reaching the maximum value for 3,4,6-tribromocatechol. The T2 for 1H is also enhanced by a factor of 4, varying by ∼14 s across the series. When complexed, the impact of the patterning design strategy on nuclear spin dynamics is amplified and 1H T1 and T2 values vary by over an order of magnitude. Importantly, the general trends observed in the ligands also match those when complexed. Hence, these results demonstrate a new design principle to control 1H spin dynamics in metal complexes through pattern-based design strategies in the ligand shell.more » « less
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