A fundamental challenge in neuroscience is to estimate the structure of white matter connectivity inthehuman brainorconnectomes [14,29]. Connectomes aremadeupofneuronal axonbundles wrapped with myelin sheaths, called fascicles, and connect different areas ofthe brain.

Diffusion-weighted MRI allows us to estimate structural brain connections in-vivo by measuring the diffusion of water molecules at different spatial directions.

A data-driven approach, however, could overcome limitations in such rule-based approaches and improve precision mappings for individuals.

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al. proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spike-sorting).

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization [1, 2]. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al. [3] proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spikesorting).

Following limb amputation, neural signals for limb functions persist in the residual peripheral nerves. Targeted muscle reinnervation (TMR) allows to redirected these signals into spare muscles to recover the neural information through electromyography (EMG). However, a significant challenge arises in separating distinct neural commands redirected from the transferred nerves to the muscles. Disentangling overlapping signals from EMG recordings remains complex, as they can contain mixed neural information that complicates limb function interpretation. To address this challenge, Regenerative Peripheral Nerve Interfaces (RPNIs) surgically partition the nerve into individual fascicles that reinnervate specific muscle grafts, isolating distinct neural sources for more precise control and interpretation of EMG signals. We introduce a novel biointerface that combines TMR surgery of polyvalent nerves with a high-density micro-electrode array implanted at a single site within a reinnervated muscle. Instead of surgically identifying distinct nerve fascicles, our approach separates all neural signals that are re-directed into a single muscle, using the high spatio-temporal selectivity of the micro-electrode array and mathematical source separation methods. We recorded EMG signals from four reinnervated muscles while volunteers performed phantom limb tasks. The decomposition of these signals into motor unit activity revealed distinct clusters of motor neurons associated with diverse functional tasks. Notably, our method enabled the extraction of multiple neural commands within a single reinnervated muscle, eliminating the need for surgical nerve division. This approach not only has the potential of enhancing prosthesis control but also uncovers mechanisms of motor neuron synergies following TMR, providing valuable insights into how the central nervous system encodes movement after reinnervation.

Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models.

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization [1, 2]. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al. [3] proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spikesorting).

In addition to the Olympics and Paralympics, there's another epic celebration of human fortitude: The Cybathlon, otherwise known as the Cyborg Olympics. According to Max Ortiz-Catalan, a bionics engineer at the Chalmers University of Technology in Sweden, it's "the Olympics for cyborgs, where technologies are used to overcome disabilities." Unlike the other events, the Cybathlon commemorates new prosthetic technologies and runs timed competitions ranging from biking to hanging laundry. Hanging up T-shirts while wearing an arm prosthesis is notably difficult. These prostheses can be bulky and hard to maneuver, with a limited range of motion.

This paper discusses and contests the claims of ``Soft Pneumatic Actuator Fascicles for High Force and Reliability'' a research article which was originally published in the March 2017 issue of the Journal Soft Robotics. The original paper claims that the summed forces of multiple thin-walled extending McKibben muscles are greater than a volumetrically equivalent actuator of the same length at the same pressure. The original paper also claims that the purported benefit becomes more pronounced as the number of smaller actuators is increased. Using reasonable assumptions, the analysis of this paper shows that the claims of the original paper violate the law of conservation of energy. This paper also identifies errors in the original methodology that may have led to the erroneous conclusions of the original paper. The goal of this paper is to correct the record and to provide a more accurate framework for considering fascicles used in soft pneumatic actuator packs.