3D microscopy and deep learning classify adipose tissue immune microenvironments associated with obesity comorbidities.

Morphological brain network plays a key role in investigating abnormalities in neurological diseases such as mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, most of the morphological brain network construction methods only considered a single morphological feature. Each type of morphological feature has specific neurological and genetic underpinnings. A combination of morphological features has been proven to have better diagnostic performance compared with a single feature, which suggests that an individual morphological brain network based on multiple morphological features would be beneficial in disease diagnosis. Here, we proposed a novel method to construct individual morphological brain networks for two datasets by calculating the exponential function of multivariate Euclidean distance as the evaluation of similarity between two regions. The first dataset included 24 healthy subjects who were scanned twice within a 3-month period. The topological properties of these brain networks were analyzed and compared with previous studies that used different methods and modalities. Small world property was observed in all of the subjects, and the high reproducibility indicated the robustness of our method. The second dataset included 170 patients with MCI (86 stable MCI and 84 progressive MCI cases) and 169 normal controls (NC). The edge features extracted from the individual morphological brain networks were used to distinguish MCI from NC and separate MCI subgroups (progressive vs. stable) through the support vector machine in order to validate our method. The results showed that our method achieved an accuracy of 79.65% (MCI vs. NC) and 70.59% (stable MCI vs. progressive MCI) in a one-dimension situation. In a multiple-dimension situation, our method improved the classification performance with an accuracy of 80.53% (MCI vs. NC) and 77.06% (stable MCI vs. progressive MCI) compared with the method using a single feature. The results indicated that our method could effectively construct an individual morphological brain network based on multiple morphological features and could accurately discriminate MCI from NC and stable MCI from progressive MCI, and may provide a valuable tool for the investigation of individual morphological brain networks.

Single-image super-resolution (SISR) is a classic image restoration operation that seeks to estimate a high-resolution (HR) image from an observed low-resolution (LR) one.¹ A variety of methods have been developed to achieve this goal, such as filtering and interpolation-based approaches² and more formal regularized inverse problem-based formulations,^3,4 to name a few. Recently, deep learning-based image super-resolution (DL-SR) methods have been widely employed and have shown great promise for SISR in terms of traditional image quality (IQ) metrics such as mean square error (MSE), structural similarity index metric (SSIM), and peak-signal-to-noise ratio (PSNR).^{5 – 8}

Temporal correlation analysis of spontaneous brain activity (e.g., Pearson “functional connectivity,” FC) has provided insights into the functional organization of the human brain. However, bivariate analysis techniques such as this are often susceptible to confounding physiological processes (e.g., sleep, Mayer-waves, breathing, motion), which makes it difficult to accurately map connectivity in health and disease as these physiological processes affect FC. In contrast, a multivariate approach to imputing individual neural networks from spontaneous neuroimaging data could be influential to our conceptual understanding of FC and provide performance advantages. Therefore, we analyzed neural calcium imaging data from Thy1-GCaMP6f mice while either awake, asleep, anesthetized, during low and high bouts of motion, or before and after photothrombotic stroke. A linear support vector regression approach was used to determine the optimal weights for integrating the signals from the remaining pixels to accurately predict neural activity in a region of interest (ROI). The resultant weight maps for each ROI were interpreted as multivariate functional connectivity (MFC), resembled anatomical connectivity, and demonstrated a sparser set of strong focused positive connections than traditional FC. While global variations in data have large effects on standard correlation FC analysis, the MFC mapping methods were mostly impervious. Lastly, MFC analysis provided a more powerful connectivity deficit detection following stroke compared to traditional FC.

In medical imaging, it is sometimes desirable to acquire high resolution images that reveal anatomical and physiological information to support clinical practice such as diagnosis and image-guided therapies. However, for certain imaging modalities (i.e., magnetic resonance imaging (MRI)), acquiring high resolution images can be a very time-consuming and resource-intensive process. One popular solution recently developed is to create a high resolution version of the acquired low-resolution image by use of deep image super-resolution (DL-SR) methods. It has been demonstrated in literature that deep super-resolution networks can improve the image quality measured by traditional physical metrics such as mean square error (MSE), structural similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR). However, it is not clear how well these metrics quantify the diagnostic value of the generated SR images. Here, a task-based super-resolution (SR) image quality assessment is conducted to quantitatively evaluate the efficiency and performance of DL-SR methods. A Rayleigh task is designed to investigate the impact of signal length and super-resolution network complexity on s binary detection performance. Numerical observers (NOs) including the regularized Hotelling Observer (RHO), the anthropomorphic Gabor channelized observers (Gabor CHO) and the ResNet-approximated ideal observer (ResNet-IO) are implemented to assess the Rayleigh task performance. For the datasets considered in this study, little to no improvement in task performance of the considered NOs due to the considered DL-SR SR networks, despite substantial improvement in traditional IQ metrics.