Lipeng Ning

Purpose: Combining multi‐site diffusion MRI (dMRI) data is hindered by inter‐scanner variability, which confounds subsequent analysis. Previous harmonization methods require large, matched or traveling human subjects from multiple sites, which are impractical to acquire in many situations. This study aims to develop a deep learning‐based dMRI harmonization framework that eliminates the reliance on multi‐site in‐vivo traveling human data for training. Methods: HARP employs a voxel‐wise 1D neural network trained on an easily transportable diffusion phantom. The model learns relationships between spherical harmonics coefficients of different sites without memorizing spatial structures. Results: HARP reduced inter‐scanner variability levels significantly in various measures. Quantitatively, it decreased inter‐scanner variability as measured by standard error in FA (12%), MD (10%), and GFA (30%) with scan‐rescan standard error as the baseline, while preserving fiber orientations and tractography after harmonization. Conclusion: We believe that HARP represents an important first step toward dMRI harmonization using only phantom data, thereby obviating the need for complex, matched in vivo multi‐site cohorts. This phantom‐only strategy substantially enhances the feasibility and scalability of quantitative dMRI for large‐scale clinical studies.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive technique used to characterize tissue microstructure by measuring the diffusion of water molecules. Conventional Q-space trajectory imaging (QTI) estimates diffusion using low-order moments; however, it often neglects higher-order moments, such as the skewness tensor, resulting in an incomplete representation of diffusion asymmetry and potential estimation bias. In this work, we propose Q-space trajectory imaging with Skewness Tensor Constraints (QTI-STC), a method that incorporates higher-order skewness tensors under positivity constraints to mitigate deviations in the estimation of lower-order moments caused by the omission of higher-order asymmetry information. Furthermore, we introduce linear trace-weighted (LF) and quadratic trace-weighted filters (QF) to enhance high-diffusion components while suppressing low-diffusion components. Extensive experiments conducted on public, noisy, and synthetic datasets demonstrate that our method yields estimates closer to the ground truth on synthetic data and exhibits superior robustness in noisy conditions.

Relaxation-diffusion distribution characterizes tissue microstructure using multi-contrast MRI data without using a multi-compartment model. This work applies and generalizes two nonlinear spectral estimation algorithms to compute relaxation-diffusion distributions and compares their performances with the standard linear inverse method. The first algorithm employs maximum entropy (MaxEnt) estimation, extending previous methods by incorporating measurement noise for improved robustness. The second algorithm is based on the MUltiple SIgnal Classification (MUSIC) subspace spectral estimation technique, enabling pseudo-spectral estimation of multi-exponential signals sampled on regular grids without solving optimization problems. Both methods were compared against the basis representation technique and the nonnegative least squares (NNLS) method using synthetic and in vivo data. MaxEnt demonstrated superior spectral resolution compared to other methods. Meanwhile, the multidimensional MUSIC algorithm provided accurate estimations but required a higher signal-to-noise ratio. MaxEnt and MUSIC improve computational efficiency, especially when a high-resolution sampling grid is required for the density functions.

BACKGROUND: We aimed to characterize factors associated with the under-studied complication of cognitive decline in aging people with long-duration type 1 diabetes (T1D). METHODS: Joslin “Medalists” (n = 222; T1D ≥ 50 years) underwent cognitive testing. Medalists (n = 52) and age-matched nondiabetic controls (n = 20) underwent neuro- and retinal imaging. Brain pathology (n = 26) was examined. Relationships among clinical, cognitive, and neuroimaging parameters were evaluated. RESULTS: Compared with controls, Medalists had worse psychomotor function and recall, which associated with female sex, lower visual acuity, reduced physical activity, longer diabetes duration, and higher inflammatory cytokines. On neuroimaging, compared with controls, Medalists had significantly lower total and regional brain volumes, equivalent to 9 years of accelerated aging, but small vessel disease markers did not differ. Reduced brain volumes associated with female sex, reduced psychomotor function, worse visual acuity, longer diabetes duration, and higher inflammation, but not with glycemic control. Worse cognitive function, lower brain volumes, and diabetic retinopathy correlated with thinning of the outer retinal nuclear layer. Worse baseline visual acuity associated with declining psychomotor function in longitudinal analysis. Brain volume mediated the association between visual acuity and psychomotor function by 57%. Brain pathologies showed decreased volumes, but predominantly mild vascular or Alzheimer’s-related pathology. CONCLUSION: To our knowledge, this is the first comprehensive study of cognitive function, neuroimaging, and pathology in aging T1D individuals demonstrated that cognitive decline was related to parenchymal rather than neurovascular abnormalities, unlike type 2 diabetes, suggestive of accelerated aging in T1D. Improving visual acuity could perhaps be an important preventive measure against cognitive decline in people with T1D. FUNDING: The Beatson Foundation, NIH/NIDDK grants 3P30DK036836-34S1 and P30DK036836-37, and Mary Iacocca fellowships.

Reinforcement Learning (RL) is crucial in decision optimization, but its inherent complexity often presents challenges in interpretation and communication. Building upon AutoDOViz—an interface that pushed the boundaries of Automated RL for Decision Optimization—this article unveils an open-source expansion with a web-based platform for RL. Our work introduces a taxonomy of RL visualizations and launches a dynamic web platform, leveraging backend flexibility for AutoRL frameworks like ARLO and Svelte.js for a smooth interactive user experience in the front end. Since AutoDOViz is not open-source, we present AutoRL X, a new interface designed to visualize RL processes. AutoRL X is shaped by the extensive user feedback and expert interviews from AutoDOViz studies, and it brings forth an intelligent interface with real-time, intuitive visualization capabilities that enhance understanding, collaborative efforts, and personalization of RL agents. Addressing the gap in accurately representing complex real-world challenges within standard RL environments, we demonstrate our tool’s application in healthcare, explicitly optimizing brain stimulation trajectories. A user study contrasts the performance of human users optimizing electric fields via a 2D interface with RL agents’ behavior that we visually analyze in AutoRL X, assessing the practicality of automated RL. All our data and code is openly available at: https://github.com/lorifranke/autorlx .

The superficial white matter (SWM) consists of numerous short‐range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly‐curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra‐high‐resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high‐resolution 0.76‐mm isotropic acquisition using the generalized slice‐dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25‐mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP‐YA) database. Our results demonstrate significant differences in the cortico‐cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract‐tracing literature. We find that the high‐resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.