Imaging peripheral nerve micro-anatomy with MUSE, 2D and 3D approaches (2022) Scientific Reports, 12 (1), art. no. 10205, .
Kolluru, C.a , Todd, A.b , Upadhye, A.R.a c , Liu, Y.a , Berezin, M.Y.d , Fereidouni, F.e , Levenson, R.M.e , Wang, Y.f , Shoffstall, A.J.a c , Jenkins, M.W.a g , Wilson, D.L.a f
Understanding peripheral nerve micro-anatomy can assist in the development of safe and effective neuromodulation devices. However, current approaches for imaging nerve morphology at the fiber level are either cumbersome, require substantial instrumentation, have a limited volume of view, or are limited in resolution/contrast. We present alternative methods based on MUSE (Microscopy with Ultraviolet Surface Excitation) imaging to investigate peripheral nerve morphology, both in 2D and 3D. For 2D imaging, fixed samples are imaged on a conventional MUSE system either label free (via auto-fluorescence) or after staining with fluorescent dyes. This method provides a simple and rapid technique to visualize myelinated nerve fibers at specific locations along the length of the nerve and perform measurements of fiber morphology (e.g., axon diameter and g-ratio). For 3D imaging, a whole-mount staining and MUSE block-face imaging method is developed that can be used to characterize peripheral nerve micro-anatomy and improve the accuracy of computational models in neuromodulation. Images of rat sciatic and human cadaver tibial nerves are presented, illustrating the applicability of the method in different preclinical models. © 2022, The Author(s).
Normal aging in mice is associated with a global reduction in cortical spectral power and network-specific declines in functional connectivity (2022) NeuroImage, 257, art. no. 119287, .
Albertson, A.J.a , Landsness, E.C.a , Tang, M.J.b , Yan, P.a , Miao, H.a , Rosenthal, Z.P.c , Kim, B.d , Culver, J.C.e f g h , Bauer, A.Q.e f , Lee, J.-M.a e f
Normal aging is associated with a variety of neurologic changes including declines in cognition, memory, and motor activity. These declines correlate with neuronal changes in synaptic structure and function. Degradation of brain network activity and connectivity represents a likely mediator of age-related functional deterioration resulting from these neuronal changes. Human studies have demonstrated both general decreases in spontaneous cortical activity and disruption of cortical networks with aging. Current techniques used to study cerebral network activity are hampered either by limited spatial resolution (e.g. electroencephalography, EEG) or limited temporal resolution (e.g., functional magnetic resonance imaging, fMRI). Here we utilize mesoscale imaging of neuronal activity in Thy1-GCaMP6f mice to characterize neuronal network changes in aging with high spatial resolution across a wide frequency range. We show that while evoked activity is unchanged with aging, spontaneous neuronal activity decreases across a wide frequency range (0.01–4 Hz) involving all regions of the cortex. In contrast to this global reduction in cortical power, we found that aging is associated with functional connectivity (FC) deterioration of select networks including somatomotor, cingulate, and retrosplenial nodes. These changes are corroborated by reductions in homotopic FC and node degree within somatomotor and visual cortices. Finally, we found that whole-cortex delta power and delta band node degree correlate with exploratory activity in young but not aged animals. Together these data suggest that aging is associated with global declines in spontaneous cortical activity and focal deterioration of network connectivity, and that these reductions may be associated with age-related behavioral declines. © 2022
Data-driven uncertainty quantification in computational human head models (2022) Computer Methods in Applied Mechanics and Engineering, 398, art. no. 115108, .
Upadhyay, K.a b , Giovanis, D.G.c , Alshareef, A.d , Knutsen, A.K.e , Johnson, C.L.f , Carass, A.d , Bayly, P.V.g , Shields, M.D.c , Ramesh, K.T.a b
Computational models of the human head are promising tools for estimating the impact-induced response of the brain, and thus play an important role in the prediction of traumatic brain injury. The basic constituents of these models (i.e., model geometry, material properties, and boundary conditions) are often associated with significant uncertainty and variability. As a result, uncertainty quantification (UQ), which involves quantification of the effect of this uncertainty and variability on the simulated response, becomes critical to ensure reliability of model predictions. Modern biofidelic head model simulations are associated with very high computational cost and high-dimensional inputs and outputs, which limits the applicability of traditional UQ methods on these systems. In this study, a two-stage, data-driven manifold learning-based framework is proposed for UQ of computational head models. This framework is demonstrated on a 2D subject-specific head model, where the goal is to quantify uncertainty in the simulated strain fields (i.e., output), given variability in the material properties of different brain substructures (i.e., input). In the first stage, a data-driven method based on multi-dimensional Gaussian kernel-density estimation and diffusion maps is used to generate realizations of the input random vector directly from the available data. Computational simulations of a small number of realizations provide input–output pairs for training data-driven surrogate models in the second stage. The surrogate models employ nonlinear dimensionality reduction using Grassmannian diffusion maps, Gaussian process regression to create a low-cost mapping between the input random vector and the reduced solution space, and geometric harmonics models for mapping between the reduced space and the Grassmann manifold. It is demonstrated that the surrogate models provide highly accurate approximations of the computational model while significantly reducing the computational cost. Monte Carlo simulations of the surrogate models are used for uncertainty propagation. UQ of the strain fields highlights significant spatial variation in model uncertainty, and reveals key differences in uncertainty among commonly used strain-based brain injury predictor variables. © 2022 Elsevier B.V.
Classifying the Severity of Cubital Tunnel Syndrome: A Preoperative Grading System Incorporating Electrodiagnostic Parameters (2022) Plastic and Reconstructive Surgery, 150 (1), pp. 115e-126e.
Power, H.A., Peters, B.R., Patterson, J.M.M., Padovano, W.M., Mackinnon, S.E.
BACKGROUND: Current classifications for cubital tunnel syndrome have not been shown to reliably predict postoperative outcomes. In this article, the authors introduce a new classification that incorporates clinical and electrodiagnostic parameters, including compound muscle action potential amplitude, to classify the preoperative severity of cubital tunnel syndrome. The authors compare this to established classifications and evaluate its association with patient-rated improvement. METHODS: The authors reviewed 44 patients who were treated surgically for cubital tunnel syndrome. Patients were retrospectively classified using their proposed classification and the Akahori, McGowan-Goldberg, Dellon, and Gu classifications. Correlation of grades was assessed by Spearman coefficients and agreement was assessed by weighted kappa coefficients. Patient-reported impairment was assessed using the Disabilities of the Arm, Shoulder, and Hand questionnaire before and after surgery. RESULTS: The classifications tended to grade patients in a similar way, with Spearman coefficients of 0.60 to 0.85 ( p < 0.0001) and weighted kappa coefficients of 0.46 to 0.71 ( p < 0.0001). Preoperative Disabilities of the Arm, Shoulder, and Hand scores increased with severity grade for most classifications. In multivariable analysis, the authors’ classification predicted postoperative Disabilities of the Arm, Shoulder, and Hand score improvement, whereas established classifications did not. CONCLUSIONS: Established classifications are imperfect indicators of preoperative severity. The authors introduce a preoperative classification for cubital tunnel syndrome that incorporates electrodiagnostic findings in addition to classic signs and symptoms. CLINICAL QUESTION/LEVEL OF EVIDENCE: Diagnostic, III. Copyright © 2022 by the American Society of Plastic Surgeons.
Loss of Stathmin-2, a hallmark of TDP-43-associated ALS, causes motor neuropathy (2022) Cell Reports, 39 (13), p. 111001.
Krus, K.L.a , Strickland, A.b , Yamada, Y.b , Devault, L.c , Schmidt, R.E.d , Bloom, A.J.e , Milbrandt, J.f , DiAntonio, A.g
TDP-43 mediates proper Stathmin-2 (STMN2) mRNA splicing, and STMN2 protein is reduced in the spinal cord of most patients with amyotrophic lateral sclerosis (ALS). To test the hypothesis that STMN2 loss contributes to ALS pathogenesis, we generated constitutive and conditional STMN2 knockout mice. Constitutive STMN2 loss results in early-onset sensory and motor neuropathy featuring impaired motor behavior and dramatic distal neuromuscular junction (NMJ) denervation of fast-fatigable motor units, which are selectively vulnerable in ALS, without axon or motoneuron degeneration. Selective excision of STMN2 in motoneurons leads to similar NMJ pathology. STMN2 knockout heterozygous mice, which better model the partial loss of STMN2 protein found in patients with ALS, display a slowly progressive, motor-selective neuropathy with functional deficits and NMJ denervation. Thus, our findings strongly support the hypothesis that STMN2 reduction owing to TDP-43 pathology contributes to ALS pathogenesis. Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
Real-time motion monitoring improves functional MRI data quality in infants (2022) Developmental Cognitive Neuroscience, 55, art. no. 101116, .
Badke D’Andrea, C.a b c , Kenley, J.K.d , Montez, D.F.d , Mirro, A.E.e , Miller, R.L.d , Earl, E.A.f , Koller, J.M.b , Sung, S.g h , Yacoub, E.i , Elison, J.T.g h , Fair, D.A.g h j , Dosenbach, N.U.F.c d e k l , Rogers, C.E.d , Smyser, C.D.c d e , Greene, D.J.a
Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging. © 2022 The Authors
A tissue-fraction estimation-based segmentation method for quantitative dopamine transporter SPECT (2022) Medical Physics, .
Liu, Z.a , Moon, H.S.a , Li, Z.a , Laforest, R.b , Perlmutter, J.S.b c , Norris, S.A.b c , Jha, A.K.a b
Background: Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter–single-photon emission computed tomography (DaT-SPECT) images have potential as biomarkers for measuring the severity of Parkinson’s disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT-SPECT images is challenging, a major reason being partial-volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite-sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT-SPECT images. Purpose: Design and objectively evaluate a fully automated tissue-fraction estimation-based segmentation method that segments the caudate, putamen, and GP from DaT-SPECT images. Methods: The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three-dimensional DaT-SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep-learning-based approach. Results: Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed ((Formula presented.)) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen. Conclusions: The proposed tissue-fraction estimation-based segmentation method for DaT-SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical-phantom and patient studies. © 2022 American Association of Physicists in Medicine.