Mouse anti-Human TNN monoclonal antibody, clone U30

Functional neuroimaging studies have reported task-related brain activation changes in preclinical individuals carrying the Huntington's disease (HD) gene mutation (preHD). Little is known about "task-negative" activity, i.e., patterns of task-related deactivation in preHD, and about the stability of any deactivation changes over the course of the disease. Here, we explored task-related deactivation and functional connectivity of "task-negative" networks (TNNs) in preHD followed over a time period of 2 years. Thirteen far-from-onset preHD (mean time to estimated motor onset = 19.5 years) and thirteen healthy controls were investigated. We used functional magnetic resonance imaging (fMRI), a verbal working memory task, and uni- and multivariate analysis techniques for fMRI data. Behavior was similar in preHD and controls at baseline and did not change 2 years later. At both time points, deactivation was similar in preHD and controls. Within two spatio-temporally distinct TNNs, preHD had lower functional connectivity in the posterior cingulate cortex and higher functional connectivity in the left anterior prefrontal cortex compared to controls (p < 0.05, cluster-corrected). These findings remained stable at follow-up. Anterior prefrontal connectivity correlated with disease burden scores both at baseline and at follow-up. Over time, preHD exhibited higher connectivity in a dorsal cingulate region. Functional connectivity differences within this region were inversely associated with changes of motor function. These data provide first evidence for TNN connectivity changes in preHD followed over a period of 2 years. The relationship between dorsal cingulate connectivity and motor function suggests that "task-negative" activity may capture time-sensitive neural and functional processes in preHD.

Recently, a tensor nuclear norm (TNN) based method was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem. Our method preserves the low-rank structure of a tensor by factorizing it into the product of two tensors of smaller sizes. In the optimization process, our method only needs to update two smaller tensors, which can be more efficiently conducted than computing t-SVD. Furthermore, we prove that the proposed alternating minimization algorithm can converge to a Karush-Kuhn-Tucker point. Experimental results on the synthetic data recovery, image and video inpainting tasks clearly demonstrate the superior performance and efficiency of our developed method over state-of-the-arts including the TNN and matricization methods.