Biology subjects

Napadow, V.

Publications and source records attributed to Napadow, V..

4 recordsLinked to original sources

bioRxiv2026

Physiological Cross-Sectional Surfaces: A Method for Estimating Muscle Functional Capacity from 3D Digital Models of Fiber Architecture

Physiological cross-sectional area (PCSA) is defined as the summed cross-sectional area of all muscle fibers contracting in parallel and at optimal length. PCSA is widely used, yet the conventional equations used to compute PCSA were developed from two-dimensional (2D) interpretations of muscle architecture and may not accurately represent muscle fiber cross-sections in the case of real three-dimensional (3D) geometries of muscles. Related measures of functional cross-sectional area (FCSA) and geometric cross-sectional area (GCSA) were also developed and interpreted with simplified 2D representations. Using realistic 3D muscle architectures derived from medical imaging, we sought to investigate whether conventional definitions of PCSA, FCSA, and GCSA represent the summed cross-sectional areas of all parallel muscle fibers, the fundamental definition of PCSA. We found that none of these measures consistently represented this definition. Thus, we introduce the physiological cross-sectional surface (PCSS), a curved surface within a muscle volume that is everywhere perpendicular to the local fiber direction. We estimated PCSS in 3D muscle surface meshes reconstructed from MRI data, using fiber orientations derived from Laplacian fiber reconstruction. PCSS-derived estimates were compared with PCSA, FCSA, and GCSA across six muscles representing five architectural classes. PCSS differed from all conventional measures, with the magnitude and direction of disagreement depending on muscle architecture. PCSS-to-PCSA ratios ranged from 0.771 to 1.399, while GCSA underestimated PCSS by up to a factor of 2.256 in bi- and multipennate muscles and overestimated it in muscles with more uniform fiber arrangements. PCSS demonstrated high geometric fidelity (perpendicularity>0.987) and robustness to fiber density across a tenfold range (coefficients of variation 0.39-3.95%). These findings indicate that conventional cross-sectional area measures do not consistently account for all fiber cross-sections in parallel within realistic 3D muscle geometries. PCSS provides a geometrically rigorous alternative that may improve estimation of functional muscle capacity from subject-specific imaging data.

bioengineering↗

bioRxiv2021

Correcting Cardiorespiratory Noise in Resting-state Functional MRI Data Acquired in Critically Ill Patients

Resting-state functional MRI (rs-fMRI) is being used to develop diagnostic, prognostic, and therapeutic biomarkers for critically ill patients with severe brain injuries. In studies of healthy volunteers and non-critically ill patients, prospective cardiorespiratory data are routinely collected to remove non-neuronal fluctuations in the rs-fMRI signal during analysis. However, the feasibility and utility of collecting cardiorespiratory data in critically ill patients on a clinical MRI scanner are unknown. We concurrently acquired rs-fMRI (TR=1250ms), cardiac and respiratory data in 23 critically ill patients with acute severe traumatic brain injury (TBI), and 12 healthy control subjects. We compared the functional connectivity results after denoising with cardiorespiratory data (i.e., RETROICOR) with the results obtained after standard bandpass filtering. Rs-fMRI data in 7 patients could not be analyzed due to imaging artifacts. In 6 of the remaining 16 patients (37.5%), cardiorespiratory data were either incomplete or corrupted. In both patients and control subjects, the functional connectivity corrected with RETROICOR did not significantly differ from that corrected with bandpass filtering of 0.008-0.125 Hz. Collectively, these findings suggest that there is a limited feasibility and utility to prospectively acquire high-quality cardiorespiratory data during rs-fMRI in critically ill patients with severe TBI for physiological correction.

neuroscience↗

bioRxiv2020

Dynamic brain-to-brain concordance and behavioral mirroring as a mechanism of the patient-clinician interaction

The patient-clinician interaction can powerfully shape treatment out-comes such as pain, but is often considered an intangible 'art-of-medicine', and has largely eluded scientific inquiry. Although brain correlates of social processes such as empathy and theory-of-mind have been studied using single-subject designs, the specific behavioral and neural mechanisms underpinning the patient-clinician interaction are unknown. Using a two-person interactive design, we simultaneously recorded functional MRI (i.e. hyperscanning) in patient-clinician dyads, who interacted via live video while clinicians treated evoked pain in chronic pain patients. Our results show that patient analgesia is mediated by patient-clinician nonverbal behavioral mirroring and brain-to-brain concordance in circuitry implicated in theory-of-mind and social mirroring. Dyad-based analyses showed extensive dynamic coupling of these brain nodes with the partners' brain activity, yet only in dyads where clinical rapport had been established prior to the interaction. These findings point to a putatively key brain-behavioral mechanism for therapeutic alliance and psychosocial analgesia.

neuroscience↗

bioRxiv2020

Inpainting as a Technique for Estimation of Missing Voxels in Chemical Shift Imaging

Issues with model fitting (i.e. suboptimal standard deviation, linewidth/full-width-at-half-maximum, and/or signal-to-noise ratio) in multi-voxel MRI spectroscopy, or chemical shift imaging (CSI), can result in the significant loss of usable voxels. A potential solution to minimize this problem is to estimate the value of unusable voxels by utilizing information from reliable voxels in the same image. We assessed an image restoration method called inpainting as a tool to restore unusable voxels and compared it with traditional interpolation methods (nearest neighbor, trilinear interpolation and tricubic interpolation). We applied these techniques to N-acetylaspartate (NAA) spectroscopy maps from a CSI dataset. Inpainting exhibited superior performance (lower normalized root-mean-square errors, NRMSE) compared to all other methods considered (ps<0.001). Inpainting maintained its superiority whether the previously unusable voxels were randomly distributed or located in regions most commonly affected by voxel loss in real-world data. Clinical RelevanceThe presence of missing voxels can be problematic, particularly when data are analyzed in standard space, given that only voxels that are contributed to by all participants can be interrogated in these analyses. Inpainting is a promising approach for recovering unusable or missing voxels in voxelwise analyses, particularly in imaging modalities characterized by low SNR such as CSI.

bioengineering↗